Towards robust subspace recovery via sparsity-constrained latent low-rank representation

Li, Ping; Bu, Jiajun; Yu, Jun; Chen, Chun

doi:10.1016/j.jvcir.2015.06.012

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…On the other hand, due to process, instrument electromagnetic interference, and other factors, noise pollution will be produced inevitably in the process of genetic sequencing. To overcome the limitation of LRR, [25] proposed a method of Lat-LRR which expressed the original observation data X as a linear combination of principal feature X Z and latent feature L X for feature extraction. Considering the characteristics of heavy noise in gene expression profile, we added sparsity constraints to the model to construct the following Lat-LRR function:

\begin{matrix} T1 \\ O_{2} = \{\begin{matrix} normalm normali normaln & {(‖‖|, boldZ)}_{*} + {(‖‖|, boldL)}_{*} + λ {(‖‖|, boldE)}_{normal2,1} \\ normals . normalt . & boldX = boldX boldZ + boldL boldX + boldE . \end{matrix} \end{matrix}

…”

Section: Related Workmentioning

confidence: 99%

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Yang

2017

BioMed Research International

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Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the original data effectively and improve the final clustering accuracy while segmenting the subspace. The introduction of nonnegative constraints makes the computation with some sparsity, which enhances the robustness of the algorithm. Different from Lat-LRR, a new solution model is adopted to simplify the computational complexity. The experimental results show that the proposed algorithm has good feature extraction performance for the heavy redundancy and noise gene expression profile, which, compared with LRR and Lat-LRR, can achieve better clustering accuracy.

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\begin{matrix} T1 \\ O_{2} = \{\begin{matrix} normalm normali normaln & {(‖‖|, boldZ)}_{*} + {(‖‖|, boldL)}_{*} + λ {(‖‖|, boldE)}_{normal2,1} \\ normals . normalt . & boldX = boldX boldZ + boldL boldX + boldE . \end{matrix} \end{matrix}

…”

Section: Related Workmentioning

confidence: 99%

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Yang

2017

BioMed Research International

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“…So, LRR may not represent the subspaces effectively, and the recovery robustness may be weakened. LLRR can be regarded as an enhanced version of LRR, which constructs the dictionary A using both observed data X o and unobserved hidden data X H ; it is more accurate and robust to noise than LRR for subspace representation [34,35]. To resolve the problem of insufficient sampling and to improve the robustness to noise corruption, LLRR is exploited to extract suitable image features during the PIH generation.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

2018

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Robustness and discriminability are the two most important features of perceptual image hashing (PIH) schemes. In order to achieve a good balance between perceptual robustness and discriminability, a novel PIH algorithm is proposed by combining latent low-rank representation (LLRR) and rotation invariant uniform local binary patterns (RiuLBP). LLRR is first applied on resized original images to the principal feature matrix and to the salient feature matrix, since it can automatically extract salient features from corrupted images. Following this, Riulocal bin features are extracted from each non-overlapping block of the principal feature matrix and of the salient feature matrix, respectively. All features are concatenated and scrambled to generate final binary hash code. Experimental results show that the proposed hashing algorithm is robust against many types of distortions and attacks, such as noise addition, low-pass filtering, rotation, scaling, and JPEG compression. It outperforms other local binary patterns (LBP) based image hashing schemes in terms of perceptual robustness and discriminability.

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