Classification via Subspace Learning Machine (SLM): Methodology and Performance Evaluation

Abstract

Inspired by the decision learning process of multilayer per-ceptron (MLP) and decision tree (DT), a new classification model, named the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S0, by examining the discriminant power of each input feature. Then, it learns projections of features in S0 to yield 1D subspaces and finds the optimal partition for each. A criterion is developed to choose the best q partitions that yield 2q partitioned subspaces. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops, and each leaf node makes a prediction. The ensembles of SLM trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM trees, ensembles and classical classifiers.