Methodology of soft partition for image classification

Abstract

The idea of subspace learning machine (SLM) has been a powerful tool for machine learning (ML), and it has been successfully applied to the task of image classification. Recently, a novel SLM method was proposed, which (i) projects high-dimensional feature vectors into a 1D feature subspace, and (ii) partitions it into two disjoint sets. SLM with soft partitioning (SLM/SP) extends this approach by learning an adaptive soft decision tree (SDT) structure using local greedy subspace partitioning. After meeting the stopping criteria for all child nodes and determining the tree structure, it updates all projection vectors (PVs) globally. It enables efficient training, high classification accuracy, and a small model size. It is applied to experimental data to show its performance as a lightweight and high-performance classification method.