Data Representation in Machine Learning

In implementing most of the machine learning algorithms, we represent each data point with a feature vector as the input. A vector is basically an array of numerics, or in physics, an object with magnitude and direction. How do we represent our business data in terms of a vector?

Primitive Feature Vector

Whether the data are measured observations, or images (pixels), free text, factors, or shapes, they can be categorized into four following types:

  1. Categorical data
  2. Binary data
  3. Numerical data
  4. Graphical data

The most primitive representation of a feature vector looks like this:

Screen Shot 2019-09-15 at 3.58.09 PM
A typical feature vector. (Source: https://www.researchgate.net/publication/318740904_Chat_Detection_in_an_Intelligent_Assistant_Combining_Task-oriented_and_Non-task-oriented_Spoken_Dialogue_Systems/figures?lo=1)

Numerical Data

Numerical data can be represented as individual elements above (like Tweet GRU, Query GRU), and I am not going to talk too much about it.

Categorical Data

However, for categorical data, how do we represent them? The first basic way is to use one-hot encoding:

Screen Shot 2019-09-15 at 4.02.51 PM
One-hot encoding of categorical data (Source: https://developers.google.com/machine-learning/data-prep/transform/transform-categorical)

For each type of categorical data, each category has an integer code. In the figure above, each color has a code (0 for red, 1 for orange etc.) and they will eventually be transformed to the feature vector on the right, with vector length being the total number of categories found in the data, and the element will be filled with 1 if it is of that category. This allows a natural way of dealing with missing data (with all elements 0) and multi-category (with multiple non-zeros).

In natural language processing, the bag-of-words model is often used to represent free-text data, which is the one-hot encoding above with words as the categories. It is a good way as long as the order of the words does not matter.

Binary Data

For binary data, it can be easily represented by one element, either 1 or 0.

Graphical Data

Graphical data are best represented in terms of graph Laplacian and adjacency matrix. Refer to a previous blog article for more information.

Shortcomings

A feature vector can be a concatenation of various features in terms of all these types except graphical data.

However, such representation that concatenates all the categorical, binary, and numerical fields has a lot of shortcomings:

  1. Data with different categories are often seen as orthogonal, i.e., perfectly dissimilar.  It ignores the correlation between different variables. However, it is a very big assumption.
  2. The weights of different fields are not considered.
  3. Sometimes if the numerical values are very large, it outweighs other categorical data in terms of influence in computation.
  4. Data are very sparse, costing a lot of memory waste and computing time.
  5. It is unknown whether some of the data are irrelevant.

Modifying Feature Vectors

In light of the shortcomings, to modify the feature factors, there are three main ways of dealing with this:

  1. Rescaling: rescaling all of some of the elements, or reweighing, to adjust the influence from different variables.
  2. Embedding: condensing the information into vectors of smaller lengths.
  3. Sparse coding: deliberately extend the vectors to a larger length.

Rescaling

Rescaling means rescaling all or some of the elements in the vectors. Usually there are two ways:

  1. Normalization: normalizing all the categories of one feature to having the sum of 1.
  2. Term frequency-inverse document frequency (tf-idf): weighing the elements so that the weights are heavier if the frequency is higher and it appears in relatively few documents or class labels.

Embedding

Embedding means condensing a sparse vector to a smaller vector. Many sparse elements disappear and information is encoded inside the elements. There are rich amount of work on this.

  1. Topic models: finding the topic models (latent Dirichlet allocation (LDA),  structural topic models (STM) etc.) and encode the vectors with topics instead;
  2. Global dimensionality reduction algorithms: reducing the dimensions by retaining the principal components of the vectors of all the data, e.g., principal component analysis (PCA), independent component analysis (ICA), multi-dimensional scaling (MDS) etc;
  3. Local dimensionality reduction algorithms: same as the global, but these are good for finding local patterns, where examples include t-Distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP);
  4. Representation learned from deep neural networks: embeddings learned from encoding using neural networks, such as auto-encoders, Word2Vec, FastText, BERT etc.
  5. Mixture Models: Gaussian mixture models (GMM), Dirichlet multinomial mixture (DMM) etc.
  6. Others: Tensor decomposition (Schmidt decomposition, Jennrich algorithm etc.), GloVe etc.

Sparse Coding

Sparse coding is good for finding basis vectors for dense vectors.

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Author-Topic Models in gensim

Recently, gensim, a Python package for topic modeling, released a new version of its package which includes the implementation of author-topic models.

The most famous topic model is undoubtedly latent Dirichlet allocation (LDA), as proposed by David Blei and his colleagues. Such a topic model is a generative model, described by the following directed graphical models:

lda_pic

In the graph, \alpha and \beta are hyperparameters. \theta is the topic distribution of a document, z is the topic for each word in each document, \phi is the word distributions for each topic, and w is the generated word for a place in a document.

There are models similar to LDA, such as correlated topic models (CTM), where \phi is generated by not only \beta but also a covariance matrix \Sigma.

There exists an author model, which is a simpler topic model. The difference is that the words in the document are generated from the author for each document, as in the following graphical model. x is the author of a given word in the document.

author_pic

Combining these two, it gives the author-topic model as a hybrid, as shown below:

authortopic_pic

The new release of Python package, gensim, supported the author-topic model, as demonstrated in this Jupyter Notebook.

P.S.:

  • I am also aware that there is another topic model called structural topic model (STM), developed for the field of social science. However, there is no Python package supporting this, but an R package, called stm, is available for it. You can refer to their homepage too.
  • I may consider including author-topic model and STM in the next release of the Python package shorttext.

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Law Prediction

On August 1, my friends and I attended a meetup host by DC Data Science, titled “Predicting and Understanding Law with Machine Learning.” The speaker was John Nay, a Ph.D. candidate in Vanderbilt University. He presented his research which is at an application of natural language processing on legal enactment documents.

His talk was very interesting, from the similarity of presidents and the chambers, to the kind of topics each party focused on. He used a variety of techniques such as Word2Vec, STM (structural topic modeling), and some common textual and statistical analysis. It is quite a comprehensive study.

His work is demonstrated at predictgov.com. His work can be found in arXiv.

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