# Transition Subspace Learning based Least Squares Regression for Image Classification

## TL;DR

This paper introduces TSL-LSR, a novel method for image classification that learns a transition subspace with low-rank constraints to better preserve data structure and improve discriminative power.

## Contribution

The paper proposes a transition subspace learning approach with low-rank constraints for multicategory image classification, addressing overfitting and data structure preservation.

## Key findings

- Outperforms state-of-the-art algorithms on multiple datasets
- Effectively captures intrinsic data structures
- Reduces overfitting in projection learning

## Abstract

Only learning one projection matrix from original samples to the corresponding binary labels is too strict and will consequentlly lose some intrinsic geometric structures of data. In this paper, we propose a novel transition subspace learning based least squares regression (TSL-LSR) model for multicategory image classification. The main idea of TSL-LSR is to learn a transition subspace between the original samples and binary labels to alleviate the problem of overfitting caused by strict projection learning. Moreover, in order to reflect the underlying low-rank structure of transition matrix and learn more discriminative projection matrix, a low-rank constraint is added to the transition subspace. Experimental results on several image datasets demonstrate the effectiveness of the proposed TSL-LSR model in comparison with state-of-the-art algorithms