Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The signif...Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.展开更多
Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on a...Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.展开更多
Sparse subspace clustering(SSC)is a spectral clustering methodology.Since high-dimensional data are often dispersed over the union of many low-dimensional subspaces,their representation in a suitable dictionary is spa...Sparse subspace clustering(SSC)is a spectral clustering methodology.Since high-dimensional data are often dispersed over the union of many low-dimensional subspaces,their representation in a suitable dictionary is sparse.Therefore,SSC is an effective technology for diagnosing mechanical system faults.Its main purpose is to create a representation model that can reveal the real subspace structure of high-dimensional data,construct a similarity matrix by using the sparse representation coefficients of high-dimensional data,and then cluster the obtained representation coefficients and similarity matrix in subspace.However,the design of SSC algorithm is based on global expression in which each data point is represented by all possible cluster data points.This leads to nonzero terms in nondiagonal blocks of similar matrices,which reduces the recognition performance of matrices.To improve the clustering ability of SSC for rolling bearing and the robustness of the algorithm in the presence of a large number of background noise,a simultaneous dimensionality reduction subspace clustering technology is provided in this work.Through the feature extraction of envelope signal,the dimension of the feature matrix is reduced by singular value decomposition,and the Euclidean distance between samples is replaced by correlation distance.A dimension reduction graph-based SSC technology is established.Simulation and bearing data of Western Reserve University show that the proposed algorithm can improve the accuracy and compactness of clustering.展开更多
In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simul...In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix. More importantly, we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.展开更多
文摘Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.
文摘Traditional forest-fire recognition based on the characteristics of smoke, temperature and light fails to accurately detect and respond to early fires. By analyzing the characteristics of flame, the methods based on aerial image recognition have been widely used, such as RGB-based and HIS-based methods. However, these methods are susceptible to background factors causing interference and false detection. To alleviate these problems, we investigate two subspace clustering methods based on sparse and collaborative representation, respectively, to detect and locate forest fires. Firstly, subspace clustering segments flame from the whole image. Afterwards, sparse or collaborative representation is employed to represent most of the flame information in a dictionary with l1-regularization or l2-regularization term, which results in fewer reconstruction errors. Experimental results show that the proposed SSC and CSC substantially outperform the state-of-the art methods.
基金The present work is supported by the National Key R&D Program(No.2020YFB2007700)the National Natural Science Foundation of China(Nos.11790282,11802184,11902205,12002221,12032017)+1 种基金the S&T Program of Hebei(No.20310803D)the Natural Science Foundation of Hebei Province(No.A2020210028).
文摘Sparse subspace clustering(SSC)is a spectral clustering methodology.Since high-dimensional data are often dispersed over the union of many low-dimensional subspaces,their representation in a suitable dictionary is sparse.Therefore,SSC is an effective technology for diagnosing mechanical system faults.Its main purpose is to create a representation model that can reveal the real subspace structure of high-dimensional data,construct a similarity matrix by using the sparse representation coefficients of high-dimensional data,and then cluster the obtained representation coefficients and similarity matrix in subspace.However,the design of SSC algorithm is based on global expression in which each data point is represented by all possible cluster data points.This leads to nonzero terms in nondiagonal blocks of similar matrices,which reduces the recognition performance of matrices.To improve the clustering ability of SSC for rolling bearing and the robustness of the algorithm in the presence of a large number of background noise,a simultaneous dimensionality reduction subspace clustering technology is provided in this work.Through the feature extraction of envelope signal,the dimension of the feature matrix is reduced by singular value decomposition,and the Euclidean distance between samples is replaced by correlation distance.A dimension reduction graph-based SSC technology is established.Simulation and bearing data of Western Reserve University show that the proposed algorithm can improve the accuracy and compactness of clustering.
基金the National Natural Science Foundation of China (Grant Nos. 61433014 and 71601029).
文摘In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix. More importantly, we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.
