A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric ...A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.展开更多
Affinity propagation(AP)is a classic clustering algorithm.To improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP ...Affinity propagation(AP)is a classic clustering algorithm.To improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering algorithms.We propose a strategy of extending AP in non-spherical clustering by constructing category similarity of objects.Leveraging the monotonicity that the clusters’number increases with the self-similarity in AP,we propose a model selection procedure that can determine the number of clusters adaptively.For the parameters introduced by extending AP in non-spherical clustering,we provide a grid-evolving strategy to optimize them automatically.The effectiveness of AdaSAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks.Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.展开更多
Recently, some research efforts have shown that face images possibly reside on a nonlinear sub-manifold. Though Laplacianfaees method considered the manifold structures of the face images, it has limits to solve face ...Recently, some research efforts have shown that face images possibly reside on a nonlinear sub-manifold. Though Laplacianfaees method considered the manifold structures of the face images, it has limits to solve face recognition problem. This paper proposes a new feature extraction method, Two Dimensional Laplacian EigenMap （2DLEM）, which especially considers the manifold structures of the face images, and extracts the proper features from face image matrix directly by using a linear transformation. As opposed to Laplacianfaces, 2DLEM extracts features directly from 2D images without a vectorization preprocessing. To test 2DLEM and evaluate its performance, a series of ex- periments are performed on the ORL database and the Yale database. Moreover, several experiments are performed to compare the performance of three 2D methods. The experiments show that 2DLEM achieves the best performance.展开更多
基金the National Natural Science Foundation of China (No.10531070)the National Basic Research Program (973) of China (No.2006CB805901)+1 种基金the National High Technology Research and Development Program (863) of China (No.2006AA11Z209)the Grant of Science and Technology Commission of Shanghai Municipality (STCSM No.09XD1402500)
文摘In this note,we show that the image of Laplcian eigenmap in 2-dimensional Edclidean space is lied in a parabola.
基金National Hi-tech Research Development Program of China(863 Program,No.2007AA04Z421)National Natural Science Foundation of China(No.50475078,No.50775035)
文摘A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.
基金This work was supported by the National Natural Science Foundation of China(71771034,71901011,71971039)the Scientific and Technological Innovation Foundation of Dalian(2018J11CY009).
文摘Affinity propagation(AP)is a classic clustering algorithm.To improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering algorithms.We propose a strategy of extending AP in non-spherical clustering by constructing category similarity of objects.Leveraging the monotonicity that the clusters’number increases with the self-similarity in AP,we propose a model selection procedure that can determine the number of clusters adaptively.For the parameters introduced by extending AP in non-spherical clustering,we provide a grid-evolving strategy to optimize them automatically.The effectiveness of AdaSAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks.Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.
基金the National Natural Science Foundation of China(No.60441002)the National Basic Research and Development Program (973)(No.2006CB303105) and (No.2004CB318110)
文摘Recently, some research efforts have shown that face images possibly reside on a nonlinear sub-manifold. Though Laplacianfaees method considered the manifold structures of the face images, it has limits to solve face recognition problem. This paper proposes a new feature extraction method, Two Dimensional Laplacian EigenMap （2DLEM）, which especially considers the manifold structures of the face images, and extracts the proper features from face image matrix directly by using a linear transformation. As opposed to Laplacianfaces, 2DLEM extracts features directly from 2D images without a vectorization preprocessing. To test 2DLEM and evaluate its performance, a series of ex- periments are performed on the ORL database and the Yale database. Moreover, several experiments are performed to compare the performance of three 2D methods. The experiments show that 2DLEM achieves the best performance.
