Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequately explored. Most methods rely on defining a pipeline of deriving features via generative modeling and then applying clustering algorithms, separating the modeling and discriminative processes. We propose a deep hierarchical generative model which uses a mixture of discrete and continuous distributions to learn to effectively separate the different data manifolds and is trainable end-to-end. We show that by specifying the form of the discrete variable distribution we are imposing a specific structure on the model's latent representations. We test our model's discriminative performance on the task of CLL diagnosis against baselines from the field of computational FC, as well as the Variational Autoencoder literature. Variational Autoencoders (VAEs) have recently shown remarkable performance in unsupervised generative modeling of high-dimensional data generated by complex distributions BID19 , as well as semi-supervised classification where only a small subset of the data set is labeled . While the interaction between the generative and the classification capabilities of semi-supervised models has been recently explored in literature , there has been little investigation of the discriminative capabilities of a purely unsupervised framework with most works focusing on the task of unsupervised clustering BID17 BID36 BID16 . Furthermore, most of these works have been evaluated on mostly benchmark data sets which do not capture the difficulties that are often encountered on real-world data. For instance, there has been no investigation of the performance of these methods on data sets with significant class imbalance. The question that is, then, posed is whether deep generative models can be used effectively as unsupervised classifiers, which can, in essence, be cast into a question of what type of features and architectural choices are required to achieve good classification performance in an unsupervised manner.To examine the aforementioned questions we propose a deep hierarchical generative model and evaluate its performance on a difficult real world data set. In principle, we train our model in a completely unsupervised fashion, however in our experiments we rely on labeled data to measure our model's performance using suitable metrics for the problem domain, as well as derive a stopping criterion for training. Our model outperforms established state-of-the-art baselines used in the field of the problem domain. Our contributions are summarized in the following:• A framework which utilizes a hierarchy of continuous representations which conclude in a discrete variable explicitly representing categories, resulting in complex, expressive, invariant and interpretable representations BID1 , which are crucial in separating widely overlapping manifolds and achieve good classification results in significantly imbalanced data sets.• Controllable representation structure through specification of the form of the aforementioned discrete variable which better suits the task at hand given a problem scenario.
Unsupervised classification via deep generative modeling with controllable feature learning evaluated in a difficult real world task
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