Subspace Learning Machine with Soft Partitioning (SLM/SP): Methodology and Performance Benchmarking
Hongyu Fu, Xinyu Wang, Vinod Kumar Mishra, C.‐C. Jay Kuo · 2023
Subspace partitioning in a high-dimensional feature space plays a fundamental role in the design of effective classifiers. A novel subspace learning machine (SLM) that projects high-dimensional feature vectors into a 1D feature subspace and partitions it into two disjoint sets was recently proposed. As an extension, SLM with soft partitioning, denoted by SLM/SP, is proposed in this work. SLM/SP adopts the soft decision tree (SDT) data structure for decision learning. It starts by learning an adaptive tree structure by using local greedy subspace partitioning. Once the stopping criteria are met for all child nodes and the tree structure is determined, all projection vectors are updated globally. This methodology enables efficient training, high classification accuracy, and a small model size. It is shown by experimental results that an SLM/SP tree offers a lightweight and high performance classification method.