moguTDA: Python package for Simplicial Complex

It has been a while since I wrote about topological data analysis (TDA). For pedagogical reasons, a lot of the codes were demonstrated in the Github repository PyTDA. However, it is not modularized as a package, and those codes run in Python 2.7 only.

Upon a few inquiries, I decided to release the codes as a PyPI package, and I named it mogutda, under the MIT license. It is open-source, and the codes can be found at the Github repository MoguTDA. It runs in Python 2.7, 3.5, and 3.6.

For more information and simple tutorial, please refer to the documentation, or the Github page.

Continue reading “moguTDA: Python package for Simplicial Complex”

Topological Phases

Michael Kosterlitz, Duncan Haldane, and David J. Thouless are the laureates of Nobel Prize in Physics 2016, “for theoretical discoveries of topological phase transitions and topological phases of matter.” Before Thouless, topology was not known to the physics community. It is a basic knowledge nowadays, however.

I am particularly familiar with Berezinskii-Kosterlitz-Thouless phase transition. What is it? Before that, phase transitions had been studied through the framework of spontaneous symmetry breaking, employing the tools in functional field theory and renormalization group. Matter can be in either disordered state that the symmetry is not broken, or ordered state that a particular continuous symmetry is broken. Near the critical point, many observables found to exhibit long-range order, with C(r) \sim \frac{1}{r}, which are so universal that all physical systems described by the same Landau-Ginzburg-Wilson (LGW) model are found to obey it. But in lower dimensions such as d=2, proved by Mermin, Wagner, and Hohenberg, an ordered state is not stable because of its huge fluctuation.

The absence of an ordered state does not exclude the possibility of a phase transition. Berezinskii in 1971, and Kosterlitz and Thouless in 1973, suggested a phase transition that concerns the proliferation of topological objects. While the correlation must be short-ranged, i.e., C(r) \sim e^{-\frac{r}{\xi}}, a normal description using LGW model in d=2 does not permit that, unless vortices are considered. However, below a certain temperature, due to energy configuration, it is better for these vortices to be bounded. Then the material exhibits quasi-long-range order, i.e., C(r) \sim \frac{1}{r^{\alpha}}. This change in correlation function is a phase transition different from that induced by spontaneous symmetry breaking, but the proliferation of isolated topological solitons.

Their work started the study of topology in condensed matter system. Not long after this work, there was the study of topological defects in various condensed matter system, and then fractional quantum Hall effect, topological insulators, topological quantum computing, A phase in liquid crystals and helimagnets etc. “Topological” is the buzzword in condensed matter physics community nowadays. Recently, there is a preprint article connecting machine learning and topological physical state. (See: arXiv:1609.09060.)

In machine learning, deep learning is the buzzword. However, to understand how these things work, we may need a theory, or we may need to construct our own features if a large amount of data are not available. Then, topological data analysis (TDA) becomes important in the same way as condensed matter physics.

Continue reading “Topological Phases”

On Quantum Computing…

Justin Trudeau, the Canadian Prime Minister, rocked the world recently by talking about quantum computers in front of reporters at Perimeter Institute…

No fault can be found in his answer, although his answer cannot be more layman. A few physicists were challenged to give a layman introduction about quantum computers, and they are equally well. (See MacLean’s article.)

The research about quantum computers has been for decades, and it is not until recently some commercial products are available in the market.

Qubits and Quantum Entanglement

Trudeau is right that in quantum computers, a bit, called a qubit, is more than just 0 and 1, but a complicated combination of them. It can be characterized by a quantum state |\psi \rangle = \alpha |0\rangle + \beta |1\rangle, where \alpha and \beta are complex numbers. Such a state can be geometrically demonstrated as a Bloch sphere, where the possibility of a state is infinitely more than simply up and down.

963px-bloch_sphere-svg
Bloch Sphere (from Wikipedia)

Besides qubits, quantum entanglement makes quantum computing unique, because with entanglement it allows algorithms to be calculated in parallel at the same time by its nature. Quantum entanglement concerns the correlation between two qubits in a way that the quantum state of the two qubits cannot be separated into two independent quantum states, each for one qubit. There are many ways to quantify the entanglement of a bipartite state (consisting of two qubits), such as entanglement entropy (similar to Shannon entropy, see this, where the probability is on the partial density matrix calculated by Schmidt decomposition), and negativity. [Peres, 1996]

The qubits and entanglement make quantum algorithms possible. It is a big research field, bordering physics and computer science. See “Quantum Algorithm Zoo” cataloged by NIST for them. Shor’s algorithm and Grover’s algorithm are some of the famous ones.

There are two types of quantum computers in general, namely, the universal quantum computer (UQC), and non-universal adiabatic quantum computer (AQC).

Universal Quantum Computer (UQC)

UQC, the quantum computers that exploit quantum properties to perform computations that are classically infeasible, has been studied for decades in academia. Not only the quantum algorithms, the realization of hardware has always been one of the hot topics of scientific research. Although much time and money has been invested, it is still far from industrial and commercial use.

The first UQC on earth was made in MIT in 1998, by Isaac Chuang and his colleagues, using NMR techniques. [Chuang et. al., 1998] There are also other experimental realization of qubits, such as optical cavity, Josephson junction, quantum dots, nanomechanical resonator etc. Representations depend on the physics systems.

If this is successfully realized in industrialized use, it speeds up a lot of computations by fully applying the quantum algorithms.

Adiabatic Quantum Computer (AQC)

AQC does not fully exploit the quantum properties. While there are qubits, the algorithms may not be fully applied. Instead, it partially exploits quantum properties to accelerate current algorithms. It takes advantage of adiabatic theorem, which states that a slow perturbation to a quantum system remains the instantaneous eigenstate. This means, if the quantum system starts at a ground state, it stays at the ground state after the slow perturbation due to some external field. This process possibly involves quantum tunneling. AQC is actually a type of quantum annealing.

There are already some commercial applications of AQC. D-Wave Systems made their first AQC in the world, and a number of big companies such as Lockheed Martin and Google purchased them. D-Wave systems are based on Josephson junctions. It is programmable, and most suited to optimization problems. One can write their own optimization problem in terms of the Ising model:

H = \sum_i h_i x_i + \sum_{\langle i, j \rangle} J_{ij} x_i x_j,

The system will start at an initial system with its ground state, and slowly changing the interactions to this Hamiltonian. The state will be the ground state, or the optimized solution, of the problem.

We can immediately see that, in the era of big data, this is very useful as machine learning problems are optimization problems. For example, QxBranch, a startup based on Washington, DC, developed tools using D-Wave Systems to perform some machine learning algorithms.

Topological Quantum Computer

In some sense, an AQC is only a partial quantum computer, which does not employ full quantum properties. However, a UQC is hard to achieve because of quantum decoherence. A state can exist for only a very short time. There exist ways to avoid this, for example, optimal dynamic decoupling. [Fu et. al., 2009] Another fascinating idea to overcome this is topological quantum computing.

A topological quantum computer is a theoretical quantum computer that is based on anyons, a two-dimensional quasi-particles with world lines crossing over in three-dimensional world. It was first introduced in 1997 by Alexei Kitaev. The idea is to exploit the ideas of topology in anyons to preserve the state, because it is extremely costly to change the topology of a system. In 2005, Das Sarma, Freedman, and Nayak proposed using fractional quantum Hall system to achieve this. [Das Sarma et. al., 2006] Some ideas such as Majorana fermions have been proposed too. All in all, topological quantum computing is still a theoretical idea.

Continue reading “On Quantum Computing…”

Topology in Physics and Computing

Topology has been shown to reveal important information about geometry and shape from data, [Carlsson 2015][Carlsson 2009] as I have talked about in various TDA blog entries. I have also demonstrated how to describe the topology if discrete data points by constructing simplicial complexes, and then calculated the homology and Betti numbers. (I will talk about persistent homology in the future.) Dealing with discrete data points in our digital storage devices, homology is the best way to describe it.

But if you are from a physics background, you may be familiar with the concept of homotopy and fundamental group. Some physicists deal with topology without digging into advanced mathematical tools but simply through solitons. There is a well-written introduction in this blog. In the physical world, an object is said to be topological if:

  • there is a singular point that cannot be removed by a continuous deformation of field; [Mermin 1979]
  • it has a saddle-point equation of the model that is different from another object of another topology, [Rajaraman 1987] inducing different kinds of physical dynamics; [Bray 1994]
  • it can only be removed by crossing an energy barrier, which can be described by an instanton; [Calzetta, Ho, Hu 2010]
  • it can proliferate through Kosterlitz-Thouless (BKT) phase transition; [Kosterliz, Thouless 1973]
  • it can form in a system through a second-order phase transition at a finite rate, a process known as Kibble-Zurek mechanism (KZM); [Kibble 1976] [Zurek 1985] and
  • its topology can be described by a winding number. (c.f. Betti numbers in homology)

Topological objects include vortices in magnets, superfluids, superconductors, or Skyrmions in helimagnets. [Mühlbauer et. al. 2009] [Ho et. al. 2010] They may come in honeycomb order, like Abrikosov vortices in type-II superconductors, [Abrikosov 1957] and helical nanofilaments in smectics. [Matsumoto et. al. 2009] It is widely used in fractional quantum Hall effect [Tsui et. al. 1982] and topological insulators (a lot of references can be found…). They can all be described using homotopy and winding numbers. We can see that topology is useful to describe the physical world for the complexities and patterns. There are ideas in string-net theory to use topology to describe the emergence of patterns and new phases of quantum matter. [Zeng et. al. 2015] Of course, I must not omit topological quantum computing that makes the qubits immune to environmental noise. [Das Sarma, Freedman, Nayak 2005]

However in data analytics, we do not use homotopy, albeit its beauty and usefulness in the physical world. Here are some of the reasons:

  • In using homotopy, sometimes it takes decades for a lot of brains to figure out which homotopy groups to use. But in data analysis, we want to grasp the topology simply from data.
  • Homotopy deals with continuous mappings, but data are discrete. Simplicial homology captures it more easily.
  • In a physical system, we deal with usually one type of homotopy groups. But in data, we often deal with various topologies which we are not aware of in advance. Betti numbers can describe the topology easily by looking at data.
  • Of course, homotopy is difficult to compute numerically.

Afra Zomorodian argued the use of homology over homotopy in his book as well. [Zomorodian 2009]

Continue reading “Topology in Physics and Computing”

Homology and Betti Numbers

We have been talking about the elements of topological data analysis. In my previous post, I introduced simplicial complexes, concerning the ways to connect points together. In topology, it is the shape and geometry, not distances, which matter ( although while constructing the distance does play a role).

With the simplicial complexes, we can go ahead to describe its topology. We will use the techniques in algebraic topology without going into too much details. The techniques involves homology, but a full explanation of it requires the concepts of normal subgroup, kernel, image, quotient group in group theory. I will not talk about them, although I admit that there is no easy ways to talk about computational topology without touching them. I highly recommend the readers can refer to Zomorodian’s textbook for more details. [Zomorodian 2009]

I will continue with the Python class

SimplicialComplex

that I wrote in the previous blog post. Suppose we have an k-simplex, then the n-th face is any combinations with n+1 vertices. A simplicial complex is such that a face contained in a face is also a face of the complex. In this, we can define the boundary operator by

\partial_k \sigma = \sum_i (-1)^i [v_0 v_1 \ldots \hat{v}_i \ldots v_k],

where \hat{v}_i indicates the i-th vertex be removed. This operator gives all the boundary faces of a face \sigma. The faces being operated are k-faces, and this operator will be mapped to a (k-1)-faces. Then the boundary operator can be seen as a (n_k \times n_{k-1})-matrix, where n_k is the number of k-faces. This can be easily calculated with the following method:

class SimplicialComplex:
  ...
  def boundary_operator(self, i):
    source_simplices = self.n_faces(i)
    target_simplices = self.n_faces(i-1)

    if len(target_simplices)==0:
      S = dok_matrix((1, len(source_simplices)), dtype=np.float32)
      S[0, 0:len(source_simplices)] = 1
    else:
      source_simplices_dict = {}
      for j in range(len(source_simplices)):
        source_simplices_dict[source_simplices[j]] = j
      target_simplices_dict = {}
      for i in range(len(target_simplices)):
        target_simplices_dict[target_simplices[i]] = i

      S = dok_matrix((len(target_simplices), len(source_simplices)), dtype=np.float32)
      for source_simplex in source_simplices:
        for a in range(len(source_simplex)):
          target_simplex = source_simplex[:a]+source_simplex[(a+1):]
          i = target_simplices_dict[target_simplex]
          j = source_simplices_dict[source_simplex]
          S[i, j] = -1 if a % 2==1 else 1 # S[i, j] = (-1)**a
   return S

With the boundary operator, we can calculate the Betti numbers that characterize uniquely the topology of the shapes. Actually it involves the concept of homology groups that we are going to omit. To calculate the k-th Betti numbers, we calculate:

\beta_k = \text{rank} (\text{ker} \partial_k) - \text{rank} (\text{Im} \partial_{k+1}).

By rank-nullity theorem, [Jackson]

\text{rank} (\text{ker} \partial_k) +\text{rank} (\text{Im} \partial_k) = \text{dim} (\partial_k)

the Betti number is then

\beta_k = \text{dim} (\partial_k) - \text{rank}(\text{Im} \partial_k)) - \text{rank} (\text{Im} \partial_{k+1})

where the rank of the image of an operator can be easily computed using the rank method available in numpy. Then the method of calculating the Betti number is

class SimplicialComplex:
  ...
  def betti_number(self, i):
    boundop_i = self.boundary_operator(i)
    boundop_ip1 = self.boundary_operator(i+1)

    if i==0:
      boundop_i_rank = 0
    else:
      try:
        boundop_i_rank = np.linalg.matrix_rank(boundop_i.toarray())
      except np.linalg.LinAlgError:
        boundop_i_rank = boundop_i.shape[1]
    try:
      boundop_ip1_rank = np.linalg.matrix_rank(boundop_ip1.toarray())
    except np.linalg.LinAlgError:
      boundop_ip1_rank = boundop_ip1.shape[1]

    return ((boundop_i.shape[1]-boundop_i_rank)-boundop_ip1_rank)

If we draw a simplicial complex on a 2-dimensional plane, we almost have \beta_0, \beta_1 and \beta_2. $\beta_0$ indicates the number of components, \beta_1 the number of bases for a tunnel, and \beta_2 the number of voids.

Let’s have some examples. Suppose we have a triangle, not filled.

e1 = [(0, 1), (1, 2), (2, 0)]
sc1 = SimplicialComplex(e1)

Then the Betti numbers are:


In [5]: sc1.betti_number(0)
Out[5]: 1

In [6]: sc1.betti_number(1)
Out[6]: 1

In [7]: sc1.betti_number(2)
Out[7]: 0

Let’s try another example with multiple components.

e2 = [(1,2), (2,3), (3,1), (4,5,6), (6,7), (7,4)]
sc2 = SimplicialComplex(e2)

We can graphically represent it using networkx:

import networkx as nx
import matplotlib.pyplot as plt
n2 = nx.Graph()
n2.add_edges_from(sc2.n_faces(1))
nx.draw(n2)
plt.show()
Simplicial Complex of e2
Simplicial Complex of e2

And its Betti numbers are as follow:


In [13]: sc2.betti_number(0)
Out[13]: 2

In [14]: sc2.betti_number(1)
Out[14]: 2

In [15]: sc2.betti_number(2)
Out[15]: 0

A better illustration is the Wolfram Demonstration, titled “Simplicial Homology of the Alpha Complex”.

On top of the techniques in this current post, we can describe the homology of discrete points using persistent homology, which I will describe in my future posts. I will probably spend a post on homotopy in comparison to other types of quantitative problems.

Continue reading “Homology and Betti Numbers”

Constructing Connectivities

In my previous blog post, I introduced the newly emerged topological data analysis (TDA). Unlike most of the other data analytic algorithms, TDA, concerning the topology as its name tells, cares for the connectivity of points, instead of the distance (according to a metric, whether it is Euclidean, Manhattan, Minkowski or any other). What is the best tools to describe topology?

Physicists use a lot homotopy. But for the sake of computation, it is better to use a scheme that are suited for discrete computation. It turns out that there are useful tools in algebraic topology: homology. But to understand homology, we need to understand what a simplicial complex is.

Gunnar Carlsson [Carlsson 2009] and Afra Zomorodian [Zomorodian 2011] wrote good reviews about them, although from a different path in introducing the concept. I first followed Zomorodian’s review [Zomorodian 2011], then his book [Zomorodian 2009] that filled in a lot of missing links in his review, to a certain point. I recently started reading Carlsson’s review.

One must first understand what a simplicial complex is. Without giving too much technical details, simplicial complex is basically a shape connecting points together. A line is a 1-simplex, connecting two points. A triangle is a 2-simplex. A tetrahedron is a 3-complex. There are other more complicated and unnamed complexes. Any subsets of a simplicial complex are faces. For example, the sides of the triangle are faces. The faces and the sides are the faces of the tetrahedron. (Refer to Wolfram MathWorld for more details. There are a lot of good tutorials online.)

Implementing Simplicial Complex

We can easily encoded this into a python code. I wrote a class SimplicialComplex in Python to implement this. We first import necessary libraries:

import numpy as np
from itertools import combinations
from scipy.sparse import dok_matrix
from operator import add

The first line imports the numpy library, the second the iteration tools necessary for extracting the faces for simplicial complex, the third the sparse matrix implementation in the scipy library (applied on something that I will not go over in this blog entry), and the fourth for some reduce operation.

We want to describe the simplicial complexes in the order of some labels (which can be anything, such as integers or strings). If it is a point, then it can be represented as tuples, as below:

 (1,) 

Or if it is a line (a 1-simplex), then

 (1, 2) 

Or a 2-simplex as a triangle, then

 (1, 2, 3) 

I think you get the gist. The integers 1, 2, or 3 here are simply labels. We can easily store this in the class:

class SimplicialComplex:
  def __init__(self, simplices=[]):
    self.import_simplices(simplices=simplices)

  def import_simplices(self, simplices=[]):
    self.simplices = map(lambda simplex: tuple(sorted(simplex)), simplices)
    self.face_set = self.faces()

You might observe the last line of the codes above. And it is for calculating all the faces of this complex, and it is implemented in this way:

  def faces(self):
    faceset = set()
    for simplex in self.simplices:
      numnodes = len(simplex)
      for r in range(numnodes, 0, -1):
        for face in combinations(simplex, r):
          faceset.add(face)
    return faceset

The faces are intuitively sides of a 2D shape (2-simplex), or planes of a 3D shape (3-simplex). But the faces of a 3-simplex includes the faces of all its faces. All the faces are saved in a field called faceset. If the user wants to retrieve the faces in a particular dimension, they can call this method:

  def n_faces(self, n):
    return filter(lambda face: len(face)==n+1, self.face_set)

There are other methods that I am not going over in this blog entry. Now let us demonstrate how to use the class by implementing a tetrahedron.

sc = SimplicialComplex([('a', 'b', 'c', 'd')])

If we want to extract the faces, then enter:

sc.faces()

which outputs:

{('a',),
 ('a', 'b'),
 ('a', 'b', 'c'),
 ('a', 'b', 'c', 'd'),
 ('a', 'b', 'd'),
 ('a', 'c'),
 ('a', 'c', 'd'),
 ('a', 'd'),
 ('b',),
 ('b', 'c'),
 ('b', 'c', 'd'),
 ('b', 'd'),
 ('c',),
 ('c', 'd'),
 ('d',)}

We have gone over the basis of simplicial complex, which is the foundation of TDA. We appreciate that the simplicial complex deals only with the connectivity of points instead of the distances between the points. And the homology groups will be calculated based on this. However, how do we obtain the simplicial complex from the discrete data we have? Zomorodian’s review [Zomorodian 2011] gave a number of examples, but I will only go through two of them only. And from this, you can see that to establish the connectivity between points, we still need to apply some sort of distance metrics.

Alpha Complex

An alpha complex is the nerve of the cover of the restricted Voronoi regions. (Refer the details to Zomorodian’s review [Zomorodian 2011], this Wolfram MathWorld entry, or this Wolfram Demonstration.) We can extend the class SimplicialComplex to get a class AlphaComplex:

from scipy.spatial import Delaunay, distance
from operator import or_
from functools import partial

def facesiter(simplex):
  for i in range(len(simplex)):
    yield simplex[:i]+simplex[(i+1):]

def flattening_simplex(simplices):
  for simplex in simplices:
    for point in simplex:
      yield point

def get_allpoints(simplices):
  return set(flattening_simplex(simplices))

def contain_detachededges(simplex, distdict, epsilon):
  if len(simplex)==2:
    return (distdict[simplex[0], simplex[1]] > 2*epsilon)
  else:
    return reduce(or_, map(partial(contain_detachededges, distdict=distdict, epsilon=epsilon), facesiter(simplex)))

class AlphaComplex(SimplicialComplex):
  def __init__(self, points, epsilon, labels=None, distfcn=distance.euclidean):
    self.pts = points
    self.labels = range(len(self.pts)) if labels==None or len(labels)!=len(self.pts) else labels
    self.epsilon = epsilon
    self.distfcn = distfcn
    self.import_simplices(self.construct_simplices(self.pts, self.labels, self.epsilon, self.distfcn))

  def calculate_distmatrix(self, points, labels, distfcn):
    distdict = {}
    for i in range(len(labels)):
      for j in range(len(labels)):
        distdict[(labels[i], labels[j])] = distfcn(points[i], points[j])
    return distdict

  def construct_simplices(self, points, labels, epsilon, distfcn):
    delaunay = Delaunay(points)
    delaunay_simplices = map(tuple, delaunay.simplices)
    distdict = self.calculate_distmatrix(points, labels, distfcn)

    simplices = []
    for simplex in delaunay_simplices:
      faces = list(facesiter(simplex))
      detached = map(partial(contain_detachededges, distdict=distdict, epsilon=epsilon), faces)
      if reduce(or_, detached):
        if len(simplex)>2:
          for face, notkeep in zip(faces, detached):
            if not notkeep:
              simplices.append(face)
      else:
        simplices.append(simplex)
    simplices = map(lambda simplex: tuple(sorted(simplex)), simplices)
    simplices = list(set(simplices))

    allpts = get_allpoints(simplices)
    for point in (set(labels)-allpts):
      simplices += [(point,)]

    return simplices

The scipy package already has a package to calculate Delaunay region. The function contain_detachededges is for constructing the restricted Voronoi region from the calculated Delaunay region.

This class demonstrates how an Alpha Complex is constructed, but this runs slowly once the number of points gets big!

Vietoris-Rips (VR) Complex

Another commonly used complex is called the Vietoris-Rips (VR) Complex, which connects points as the edge of a graph if they are close enough. (Refer to Zomorodian’s review [Zomorodian 2011] or this Wikipedia page for details.) To implement this, import the famous networkx originally designed for network analysis.

import networkx as nx
from scipy.spatial import distance
from itertools import product

class VietorisRipsComplex(SimplicialComplex):
  def __init__(self, points, epsilon, labels=None, distfcn=distance.euclidean):
    self.pts = points
    self.labels = range(len(self.pts)) if labels==None or len(labels)!=len(self.pts) else labels
    self.epsilon = epsilon
    self.distfcn = distfcn
    self.network = self.construct_network(self.pts, self.labels, self.epsilon, self.distfcn)
    self.import_simplices(map(tuple, list(nx.find_cliques(self.network))))

  def construct_network(self, points, labels, epsilon, distfcn):
    g = nx.Graph()
    g.add_nodes_from(labels)
    zips = zip(points, labels)
    for pair in product(zips, zips):
      if pair[0][1]!=pair[1][1]:
        dist = distfcn(pair[0][0], pair[1][0])
        if dist<epsilon:
          g.add_edge(pair[0][1], pair[1][1])
    return g

The intuitiveness and efficiencies are the reasons that VR complexes are widely used.

For more details about the Alpha Complexes, VR Complexes and the related Čech Complexes, refer to this page.

More…

There are other commonly used complexes used, including Witness Complex, Cubical Complex etc., which I leave no introductions. Upon building the complexes, we can analyze the topology by calculating their homology groups, Betti numbers, the persistent homology etc. I wish to write more about it soon.

Taken from Wolfram Mathworld
Taken from Wolfram Mathworld

Continue reading “Constructing Connectivities”

Starting the Journey of Topological Data Analysis (TDA)

Topology has been around for centuries, but it did not catch the attention of many data analysts until recently. In an article published in Nature Scientific Reports, the authors demonstrated the power of topology in data analysis through examples including gene expression from breast rumors, voting data in the United States, and player performance data from the NBA. [Lum et. al. 2013]

As an introduction, they described topological methods “as a geometric approach to pattern or shape recognition within data.” It is true that in machine learning, we never care enough pattern recognition, but topology adds insights regarding the shapes of data that do not change with continuous deformation. For example, a circle and an ellipse have “the same topology.” The distances between data points are not as important as the shape. Traditional machine learning methods deal with feature vectors, distances, or classifications, but the topology of the data is usually discarded. Gunnar Carlsson demonstrated in a blog that a thin ellipse of data may be misrepresented as two straight parallel lines or one straight lines. [Carlsson 2015] Dimensionality reduction algorithms such as principal component analysis (PCA) often disregard the topology as well. (I heard that Kohenen’s self-organizing maps (SOM) [Kohonen 2000] retain the topology of higher dimensional data during the dimensionality reduction, but I am not confident enough to say that.)

Euler introduced the concept of topology in the 18th century. Topology has been a big subject in physics since 1950s. The string theory, as one of the many efforts in unifying gravity and other three fundamental forces, employs topological dimensions. In condensed matter physics, the fractional quantum Hall effect is a topological quantum effect. There are topological solitons [Rajaraman 1987] such as quantum vortices in superfluids, [Simula, Blakie 2006; Calzetta, Ho, Hu 2010] columns of topological solitons (believed to be Skyrmions) in helical magnets, [Mühlbauer et. al. 2009; Ho et. al. 2010; Ho 2012] hexagonal solitonic objects in smectic liquid crystals [Matsumoto et. al. 2009]… When a field becomes sophisticated, it becomes quantitative; when a quantitative field becomes sophisticated, it requires abstract mathematics such as topology for a general description. I believe analysis on any kinds of data is no exception.

There are some good reviews and readings about topological data analysis (TDA) out there, for example, the ones by Gunnar Carlsson [Carlsson 2009] and Afra Zomorodian [Zomorodian 2011]. While physicists talk about homotopy, data analysts talk about persistent homology as it is easier to compute. Data have to be described in a simplicial complex or a graph/network. Then the homology can be computed and represented in various ways such as barcodes. [Ghrist 2008] Then we extract insights about the data from it.

Topology has a steep learning curve. I am also a starter learning about this. This blog entry will not be the last talking about TDA. Therefore, I opened a new session called TDA for all of my blog entries about it. Let’s start the journey!

There is an R package called “TDA” that facilitates topological data analysis. [Fasy et. al. 2014] A taste of homology of a simplicial complex is also demonstrated in a Wolfram demo.

New-big-data-firm-to-pion-008
(Taken from TheGuardian)

Continue reading “Starting the Journey of Topological Data Analysis (TDA)”

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