Jun 11, 2015 · By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with ...
Apr 15, 2016 · By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with ...
In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with ...
By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, this approach combines feature learning with classification ...
In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with ...
In this paper, we propose a low-rank graph preserving discriminative dictionary learning (LRGPDDL) method for sparse representation-based image recognition.
Tensor low-rank representation for data recovery and clustering. P Zhou ... Integrated low-rank-based discriminative feature learning for recognition. P ...
In this paper, we propose a novel approach for low-resolution face recognition, under uncontrolled settings. Our approach first decomposes a multiple of ...
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May 7, 2019 · In this paper, we propose a robust feature subspace learning approach based on a low-rank representation. In our approach, the low-rank ...
Sep 27, 2023 · LRMPL works on both the overall reconstruction of two domains and aligns the data sharing the same label but from different domains by imposing ...