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LR-DRR optimizes the projections using a relaxed target matrix for regression based on the class label information. The proposed work extracts the projections ...
Abstract—In many pattern recognition and computer vision applications, the observed high dimensional data often contains redundant information which can ...
The least square regression (LSR) is a popular framework for multicategory classification because it has simple mathematical formulation and efficient solution.
Bibliographic details on Low-Rank Double Relaxed Regression for Discriminative Projection Learning.
A low rank discriminative least square s regression (LRDLSR) model is proposed. ... LRDLSR aims at improving the intra class similarity of the regression labels ...
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Feb 18, 2022 · proposed a double relaxed regression (DRR) method ([30]). They. 4. Page 5. pointed out that such a single transformation may be too strict to ...
[PDF] Double Relaxed Regression for Image Classification ...
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This work proposes a dual discriminative low-rank projection learning framework for robust image classification and demonstrates that the proposed method ...
A low-rank matrix is introduced to capture the common property shared by two projection matrices to preserve the similarity. •. DRLPP applies a linear subspace ...
Mar 19, 2019 · To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification.
Missing: Double | Show results with:Double
Specifically, the proposed method learns a low-rank projection and a semi-orthogonal projection to recover “clean” components from the original ...