Discriminative Subspace Emersion from learning feature relevances across different populations
This work addresses feature relevance estimation for interpretable learners in multi-population classification, but it appears incremental as it extends subspace learning with a general framework.
The paper tackles the problem of identifying the most relevant features for classification tasks shared across two disjoint populations, especially when classes overlap severely, and demonstrates that their Discriminative Subspace Emersion method accurately identifies a common subspace even with high overlap.
In a given classification task, the accuracy of the learner is often hampered by finiteness of the training set, high-dimensionality of the feature space and severe overlap between classes. In the context of interpretable learners, with (piecewise) linear separation boundaries, these issues can be mitigated by careful construction of optimization procedures and/or estimation of relevant features for the task. However, when the task is shared across two disjoint populations the main interest is shifted towards estimating a set of features that discriminate the most between the two, when performing classification. We propose a new Discriminative Subspace Emersion (DSE) method to extend subspace learning toward a general relevance learning framework. DSE allows us to identify the most relevant features in distinguishing the classification task across two populations, even in cases of high overlap between classes. The proposed methodology is designed to work with multiple sets of labels and is derived in principle without being tied to a specific choice of base learner. Theoretical and empirical investigations over synthetic and real-world datasets indicate that DSE accurately identifies a common subspace for the classification across different populations. This is shown to be true for a surprisingly high degree of overlap between classes.