Robust and stochastic sparse subspace clustering
Sparse subspace clustering (SSC) has been widely employed in machine learning and
pattern recognition, but it still faces scalability challenges when dealing with large-scale …
pattern recognition, but it still faces scalability challenges when dealing with large-scale …
A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising
Due to the high flexibility and remarkable performance, low-rank approximation has been
widely studied for color image denoising. However, existing methods usually ignore the …
widely studied for color image denoising. However, existing methods usually ignore the …
Subspace clustering based on latent low-rank representation with transformed Schatten-1 penalty function
D Hu, Q Qu, Z Liu, W Chen, Z Wang - Knowledge-Based Systems, 2024 - Elsevier
Latent low-rank representation (LLRR) is a critical method to uncover the low-dimensional
subspace structure embedded in high-dimensional data, especially when the data sampling …
subspace structure embedded in high-dimensional data, especially when the data sampling …
Enhanced video clustering using multiple riemannian manifold-valued descriptors and audio-visual information
Videos inherently blend multiple modalities in real-world scenarios, primarily visual and
auditory cues. When synergized, these cues foster enhanced data representations …
auditory cues. When synergized, these cues foster enhanced data representations …
[PDF][PDF] Nonconvex multiview subspace clustering framework with effcient method designs and theoretical analysis
Multi-view subspace clustering (MvSC) is one of the most effective methods for
understanding and processing high-dimensional data. However, existing MvSC methods …
understanding and processing high-dimensional data. However, existing MvSC methods …
Joint sparse subspace clustering via fast ℓ2, 0-norm constrained optimization
Subspace clustering gains popularity in unsupervised machine learning due to its excellent
dimensionality reduction capability and interpretability. Although existing research has made …
dimensionality reduction capability and interpretability. Although existing research has made …
PDRLRR: A novel low-rank representation with projection distance regularization via manifold optimization for clustering
H Chen, X Chen, H Tao, Z Li, B Wang - Pattern Recognition, 2024 - Elsevier
The low-rank representation (LRR) method has attracted widespread attention due to its
excellent performance in pattern recognition and machine learning. LRR-based variants …
excellent performance in pattern recognition and machine learning. LRR-based variants …
Coupled double self-expressive subspace clustering with low-rank tensor learning
T Wu, GF Lu - Expert Systems with Applications, 2024 - Elsevier
In recent years, subspace clustering (SC) methods have been widely used in machine
learning and computer vision. However, the self-expressive matrix obtained by the existing …
learning and computer vision. However, the self-expressive matrix obtained by the existing …
Latent temporal smoothness-induced Schatten-p norm factorization for sequential subspace clustering
This paper presents an innovative latent temporal smoothness-induced Schatten-p norm
factorization (SpFLTS) method aimed at addressing challenges in sequential subspace …
factorization (SpFLTS) method aimed at addressing challenges in sequential subspace …
Nonconvex Regularization with Multi‐Weighted Strategy for Real Color Image Denoising
Y Shi, T Liu, D Hu, C Li, Z Wang - International Journal of …, 2023 - Wiley Online Library
Most existing image denoising methods commonly assume that the image is contaminated
by additive white Gaussian noise (AWGN). However, real‐world color image noise exhibits …
by additive white Gaussian noise (AWGN). However, real‐world color image noise exhibits …