Latent Low-Rank Representation for subspace segmentation and feature extraction

By Guangcan Liu, Shuicheng Yan. Vision, 2011

677 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8f88bcf3b2e0fb9cb09240541d4b65bcdcd89826

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Subspace Clustering by Block Diagonal Representation (2018, background): “%) of 155 Sequences on Hopkins 155 Dataset by State-of-the-Art Methods LSA [40] SSC [10] LRR [21] LatLRR [23] LSR [28] 4.52 2.18 1.59 0.85 1.71 CASS [24] SMR [14] BD-SSC [11] BD-LRR [11] BDR-Z 1.47 1.13 1.68 0.97 0.93 TABLE 5 Clustering Error (”
MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion (2018, methodology): “So in 2011, the author [25] proposed the LatLRR theory, according to which the low-rank structure and salient structure are extracted from the raw data.”
Latent Multi-view Subspace Clustering (2017, background): “Latent Low-Rank Representation (LatLRR) [19] is built on the top of LRR [18], and constructs the dictionary by using both observed and hidden data.”
Consistent and Specific Multi-View Subspace Clustering (2018, background): “, representation learning (Liu and Yan 2011), motion segmentation (Rao et al.”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “Latent Low-Rank Representation (LatLRR) [38] is based on LRR [10] and con-”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, positive): “To this end, Low-Rank Representation (LRR) [Xia et al., 2014; Liu et al., 2010; Wang et al., 2015c; Liu and Yan, 2011; Liu et al., 2013] is proposed.”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2013, positive): “Using advances in sparse [29], [30], [31] and low-rank [32], [33], [34] recovery algorithms, the Sparse Subspace Clustering (SSC) [35], [36], [37], Low-Rank Recovery (LRR) [38], [39], [40], and Low-Rank Subspace Clustering (LRSC) [41] algorithms pose the clustering problem as one of finding a sparse”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2012, background): “Therefore, there is a need for having clustering algorithms that take into account the multisubspace structure of the data.”
Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (neutral): “Before we introduce our proposed model, in this section, we review the recent novel methods such as LRR [28] [29] [30] and graph based analysis [2] [45] [50].”
Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images (2013, positive): “More recently, low-rank representation (LRR) [24] as a subspace recovery technique has been successfully applied in several areas of image processing [25]–[28].”
Robust Recovery of Subspace Structures by Low-Rank Representation (2010, methodology): “In summary, the contributio ns of this work include: • We develop a simple yet effective method, termed LRR, which has been used to achieve state-of-the-art performance in several applications such as motion segmentation [4], image segmentation [18], saliency detection [19] and face recognition [4].”