Autoencoders to find structure in arbitrary datasets
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.. figure:: https://rawgit.com/better/crossfader/master/demo.png :align: center
An experimental model for robust dimensionality reduction of arbitrary data sets. It lets you explore the marginal distributions of all parameters with any parameter(s) fixed. It is also extremely fast at computing these probability distributions. It assumes nothing about the distribution of the features, which can have any units and have any scale.
Here is a demo_ trained on a bunch of different data sets. The demo is written in JS and uses pre-trained models.
Crossfilter_ are great at visualizing datasets and how features are correlated. Crossfilter renders real data points based on a number of feature selections. However as the number of features increase, it gets harder to find data points that fulfill all criteria. This is the
curse of dimensionality_ problem which often makes analysis of high-dimensional data hard.
This package has a different approach. It computes a statistical model of the underlying data. The downside is that you can no longer explore real data points. The upside is you can explore conditional dependencies and make predictions about data you have not observed.
It builds an
autoencoder_ that learns to reconstruct missing data.
To be able to work with any distributions, it reduces all inputs to a series of binary values. Every feature is encoded as a binary feature vector by constructing splits from the empirical distribution of the training data. The predicted probabilities for each split then gives the
The autoencoder has a series of hidden bottleneck layers (typically 2-5 layers with 20-100 units). One way to think of it is that the autoencoder finds a low-dimensional manifold in the high-dimensional space. This manifold can be highly nonlinear due to the nonlinearities in the autoencoder. The autoencoder then essentially learns a projection from the high dimensional space onto the manifold and another projection back to the original space. The dimensionality reduction is effectively a way of getting around the curse of dimensionality.
The autoencoder is trained using
Theano_ in Python. You can run it on a GPU although the speed improvements are not drastic because of some bottlenecks.
stochastic gradient descent_.
Released under the Apache 2.0 license.