Transition Subspace Learning based Least Squares Regression for Image Classification
This addresses overfitting in image classification for computer vision applications, but it appears incremental as it builds on existing least squares regression methods.
The paper tackles the problem of overfitting in multicategory image classification by proposing a transition subspace learning model with a low-rank constraint, achieving improved performance on several image datasets compared to state-of-the-art algorithms.
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