APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 1

Image Classification via Subspace Learning Machine with Soft Partitioning (SLM/SP)

Hongyu Fu, University of Southern California, USA, hongyufu@usc.edu , Xinyu Wang, University of Southern California, USA, Vinod K. Mishra, DEVCOM Army Research Laboratory, USA, C.-C. Jay Kuo, University of Southern California, USA
 
Suggested Citation
Hongyu Fu, Xinyu Wang, Vinod K. Mishra and C.-C. Jay Kuo (2024), "Image Classification via Subspace Learning Machine with Soft Partitioning (SLM/SP)", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 1, e13. http://dx.doi.org/10.1561/116.20240003

Publication Date: 21 Aug 2024
© 2024 H. Fu, Z. Wang, V. K. Mishra and C.-C. J. Kuo
 
Subjects
Classification and prediction,  Learning and statistical methods,  Object and scene recognition,  Pattern recognition and learning
 
Keywords
Subspace learning machineimage classificationmachine learningsoft decision tree
 

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In this article:
Introduction 
Background Review 
SLM Tree with Soft Partition (SLM/SP) 
Image Classification with SLM/SP 
Experiments 
Conclusion and Future Work 
Acknowledgments 
References 

Abstract

Subspace partitioning plays a fundamental role in the design of effective classification methods. A novel subspace learning machine (SLM) was recently proposed. It projects feature vectors into a 1D feature subspace and partitions it into two disjoint sets. To effectively generalize the SLM method to high-dimensional feature space, SLM with soft partitioning, denoted by SLM/SP, is proposed in this work. By incorporating the Soft Decision Tree (SDT) data structure for decision learning, the SLM/SP begins with the adaptive learning of a tree structure using local greedy subspace partitioning. Once the tree structure is finalized, all parameters are globally updated. To apply SLM/SP to image classification tasks, we propose modulated designs for the topology of the SDT and a novel module for efficient local representation learning in the subspace learning diagram. The SLM/SP methodology offers efficient training, high classification accuracy, and small model size, underscored by experimental results on image classification benchmarks.

DOI:10.1561/116.20240003