The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Zhouchen Lin - One of the best experts on this subject based on the ideXlab platform.

  • convex sparse spectral Clustering single view to multi view
    IEEE Transactions on Image Processing, 2016
    Co-Authors: Shuicheng Yan, Zhouchen Lin
    Abstract:

    Spectral Clustering (SC) is one of the most widely used methods for data Clustering. It first finds a low-dimensional embedding U of data by computing the eigenvectors of the normalized Laplacian matrix, and then performs k-means on $ {\text {U}}^\top $ to get the final Clustering Result. In this paper, we observe that, in the ideal case, $ {\text {U}} {\text {U}} ^\top $ should be block diagonal and thus sparse. Therefore, we propose the sparse SC (SSC) method that extends the SC with sparse regularization on $ {\text {U}} {\text {U}} ^\top $ . To address the computational issue of the nonconvex SSC model, we propose a novel convex relaxation of SSC based on the convex hull of the fixed rank projection matrices. Then, the convex SSC model can be efficiently solved by the alternating direction method of multipliers Furthermore, we propose the pairwise SSC that extends SSC to boost the Clustering performance by using the multi-view information of data. Experimental comparisons with several baselines on real-world datasets testify to the efficacy of our proposed methods.

Shuicheng Yan - One of the best experts on this subject based on the ideXlab platform.

  • convex sparse spectral Clustering single view to multi view
    IEEE Transactions on Image Processing, 2016
    Co-Authors: Shuicheng Yan, Zhouchen Lin
    Abstract:

    Spectral Clustering (SC) is one of the most widely used methods for data Clustering. It first finds a low-dimensional embedding U of data by computing the eigenvectors of the normalized Laplacian matrix, and then performs k-means on $ {\text {U}}^\top $ to get the final Clustering Result. In this paper, we observe that, in the ideal case, $ {\text {U}} {\text {U}} ^\top $ should be block diagonal and thus sparse. Therefore, we propose the sparse SC (SSC) method that extends the SC with sparse regularization on $ {\text {U}} {\text {U}} ^\top $ . To address the computational issue of the nonconvex SSC model, we propose a novel convex relaxation of SSC based on the convex hull of the fixed rank projection matrices. Then, the convex SSC model can be efficiently solved by the alternating direction method of multipliers Furthermore, we propose the pairwise SSC that extends SSC to boost the Clustering performance by using the multi-view information of data. Experimental comparisons with several baselines on real-world datasets testify to the efficacy of our proposed methods.

Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.

  • hyperspectral image kernel sparse subspace Clustering with spatial max pooling operation
    ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2016
    Co-Authors: Hongyan Zhang, Han Zhai, Wenzhi Liao, Liqin Cao, Liangpei Zhang, Aleksandra Pižurica
    Abstract:

    Abstract. In this paper, we present a kernel sparse subspace Clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace Clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother Clustering Result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive Clustering method for HSIs and outperforms the state-of-the-art Clustering methods.

  • spectral spatial sparse subspace Clustering for hyperspectral remote sensing images
    IEEE Transactions on Geoscience and Remote Sensing, 2016
    Co-Authors: Hongyan Zhang, Han Zhai, Liangpei Zhang
    Abstract:

    Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral–spatial sparse subspace Clustering $(\text{S}^{4}\text{C})$ algorithm for hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace Clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral Clustering is applied to the adjacent matrix to obtain the final Clustering Result. Several experiments were conducted to illustrate the performance of the proposed $\text{S}^{4}\text{C}$ algorithm.

Hongyan Zhang - One of the best experts on this subject based on the ideXlab platform.

  • hyperspectral image kernel sparse subspace Clustering with spatial max pooling operation
    ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2016
    Co-Authors: Hongyan Zhang, Han Zhai, Wenzhi Liao, Liqin Cao, Liangpei Zhang, Aleksandra Pižurica
    Abstract:

    Abstract. In this paper, we present a kernel sparse subspace Clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace Clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother Clustering Result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive Clustering method for HSIs and outperforms the state-of-the-art Clustering methods.

  • spectral spatial sparse subspace Clustering for hyperspectral remote sensing images
    IEEE Transactions on Geoscience and Remote Sensing, 2016
    Co-Authors: Hongyan Zhang, Han Zhai, Liangpei Zhang
    Abstract:

    Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral–spatial sparse subspace Clustering $(\text{S}^{4}\text{C})$ algorithm for hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace Clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral Clustering is applied to the adjacent matrix to obtain the final Clustering Result. Several experiments were conducted to illustrate the performance of the proposed $\text{S}^{4}\text{C}$ algorithm.

Tomoe Kitahara - One of the best experts on this subject based on the ideXlab platform.

  • fast algorithm for affinity propagation
    International Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Yasuhiro Fujiwara, Go Irie, Tomoe Kitahara
    Abstract:

    Affinity Propagation is a state-of-the-art Clustering method recently proposed by Frey and Dueck. It has been successfully applied to broad areas of computer science research because it has much better Clustering performance than traditional Clustering methods such as k-means. In order to obtain high quality sets of clusters, the original Affinity Propagation algorithm iteratively exchanges real-valued messages between all pairs of data points until convergence. However, this algorithm does not scale for large datasets because it requires quadratic CPU time in the number of data points to compute the messages. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same Clustering Result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of prunedmessages after the iterations to determine clusters. Experimental evaluations on several different datasets demonstrate the effectiveness of our algorithm.