scholarly journals Recursive Sample Scaling Low-Rank Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wenyun Gao ◽  
Xiaoyun Li ◽  
Sheng Dai ◽  
Xinghui Yin ◽  
Stanley Ebhohimhen Abhadiomhen
Keyword(s):  
Principal Components ◽  
Feature Space ◽  
Scaling Factor ◽  
Low Rank ◽  
Image Clustering ◽  
Subspace Segmentation ◽  
Clustering And Classification ◽  
Low Rank Representation ◽  
Segmentation Problem ◽  
Better Than

The low-rank representation (LRR) method has recently gained enormous popularity due to its robust approach in solving the subspace segmentation problem, particularly those concerning corrupted data. In this paper, the recursive sample scaling low-rank representation (RSS-LRR) method is proposed. The advantage of RSS-LRR over traditional LRR is that a cosine scaling factor is further introduced, which imposes a penalty on each sample to minimize noise and outlier influence better. Specifically, the cosine scaling factor is a similarity measure learned to extract each sample’s relationship with the low-rank representation’s principal components in the feature space. In order words, the smaller the angle between an individual data sample and the low-rank representation’s principal components, the more likely it is that the data sample is clean. Thus, the proposed method can then effectively obtain a good low-rank representation influenced mainly by clean data. Several experiments are performed with varying levels of corruption on ORL, CMU PIE, COIL20, COIL100, and LFW in order to evaluate RSS-LRR’s effectiveness over state-of-the-art low-rank methods. The experimental results show that RSS-LRR consistently performs better than the compared methods in image clustering and classification tasks.

scholarly journals Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation

2018 ◽  
Vol 8 (9) ◽  
pp. 1601
Author(s):  
Chaoqun Hong ◽  
Zhiqiang Zeng ◽  
Xiaodong Wang ◽  
Weiwei Zhuang
Keyword(s):  
Neural Networks ◽  
Age Estimation ◽  
Feature Fusion ◽  
Estimation Method ◽  
Feature Space ◽  
Image Features ◽  
Low Rank ◽  
Face Images ◽  
Low Rank Representation ◽  
Multiple Network

Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR.

scholarly journals Robust subspace segmentation via nonconvex low rank representation

2016 ◽  
Vol 340-341 ◽  
pp. 144-158 ◽  
Author(s):  
Wei Jiang ◽  
Jing Liu ◽  
Heng Qi ◽  
Qionghai Dai
Keyword(s):  
Low Rank ◽  
Subspace Segmentation ◽  
Low Rank Representation

scholarly journals Low-rank sparse subspace clustering with a clean dictionary

2021 ◽  
Vol 15 ◽  
pp. 174830262199962
Author(s):  
Cong-Zhe You ◽  
Zhen-Qiu Shu ◽  
Hong-Hui Fan
Keyword(s):  
Optimization Problem ◽  
Clustering Algorithms ◽  
Subspace Clustering ◽  
Low Rank ◽  
Data Matrix ◽  
Image Clustering ◽  
Convex Optimization Problem ◽  
Data Points ◽  
Low Rank Representation ◽  
Sparse Subspace Clustering

Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l1-norm of the data matrix while LRR promotes a low-rank structure through minimizing the nuclear norm. In this paper, considering the problem of fitting a union of subspace to a collection of data points drawn from one more subspaces and corrupted by noise, we pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise. We propose a new algorithm, named Low-Rank and Sparse Subspace Clustering with a Clean dictionary (LRS2C2), by combining SSC and LRR, as the representation is often both sparse and low-rank. The effectiveness of the proposed algorithm is demonstrated through experiments on motion segmentation and image clustering.

scholarly journals Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image

2021 ◽  
Vol 13 (7) ◽  
pp. 1372
Author(s):  
Jinhuan Xu ◽  
Liang Xiao ◽  
Jingxiang Yang
Keyword(s):  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Feature Space ◽  
Low Rank ◽  
Great Success ◽  
Clustering Methods ◽  
Hypergraph Learning ◽  
Optimal Dynamic ◽  
Low Rank Representation ◽  
Original Feature

Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information. Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality. In addition, the clustering performance obtained by the existing k-means-based clustering methods is unstable as the k-means method is sensitive to the initialization of the cluster centers. In order to address these issues, we propose a novel unified low-rank subspace clustering method with dynamic hypergraph for hyperspectral images (HSIs). In our method, the hypergraph is adaptively learned from the low-rank subspace feature, which can capture a more complex manifold structure effectively. In addition, we introduce a rotation matrix to simultaneously learn continuous and discrete clustering labels without any relaxing information loss. The unified model jointly learns the hypergraph and the discrete clustering labels, in which the subspace feature is adaptively learned by considering the optimal dynamic hypergraph with the self-taught property. The experimental results on real HSIs show that the proposed methods can achieve better performance compared to eight state-of-the-art clustering methods.

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