Multi-View Clustering and Feature Learning via Structured Sparsity

By Hua Wang, Feiping Nie, Heng Huang. 2013

315 citations recorded by the source corpus.

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Citations in the corpus

Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, methodology): “Hence we adopt the structured sparsity regularization method of [68, 70] aiming to select a number of components/layers sparsely to achieve more robust classification.”
Multi-view Subspace Clustering (2015, methodology): “The other structured sparse learning based multi-view clustering method was proposed in [22] to perform feature selection and multi-view clustering simultaneously.”
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, methodology): “Some recent methods accelerate the time complexity into O(n(3)) per iteration [3], [15], [24]–[29].”
Heterogeneous Few-Shot Model Rectification With Semantic Mapping (2020, methodology): “We requires the reconstruction coefﬁcients to be sparse to make the cross-task reconstruct coefﬁcients concentrate on the similar bases [24], [25], and the sparsity is controlled by the non-negative parameter (cid:2) .”
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (2017, background): “There is a large number of clustering algorithms in literature, which can be grouped into different perspectives, such as hierarchical [9, 51], centroid-based [20, 3, 27], graphbased [38, 28, 48, 25], sequential (temporal) [11, 37, 66], regression model based [7, 47], and subspace clustering models ”
Cross-Modality Person Re-Identification with Generative Adversarial Training (2018, background): “The former targets at learning a distance metric to handle the above robust matching problem [Zheng et al. , 2011; K¨ostinger et al. , 2012; Wang et al. , 2013; Li et al. , 2013; Xiong et al. , 2014; Liao et al. , 2015; Paisitkri-angkrai et al. , 2015; Liao and Li, 2015; Wang et al. , 2016;…”
Feature Selection (2016, methodology): “Hence, the task of multi-view feature selection arises (Feng et al. 2013; Tang et al. 2013; Wang et al. 2013; Liu et al. 2016a), which aims to select features from different feature spaces simultaneously by using their relations.”
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015, background): “(24) can be rewritten as follows, according to [31,33]:”
A Survey on Multi-View Clustering (2017, unknown): “[7] integrates multi-view information via a common indicator matrix and simultaneously select features for different data clusters by formulating the problem as follows:”
Multi-modal feature fusion for action recognition in RGB-D sequences (2014, methodology): “Multi-view structured sparsity [11] is a suitable method to apply group sparse coding over our multi-modal features.”
Clustering and projected clustering with adaptive neighbors (2014, background): “In the past decades, many clustering algorithms have been proposed, such as hierarchical clustering [10], K-means clustering [13], spectral clustering [15], spectral embedded clustering [19], support vector clustering [1], maximum margin clustering [22], initialization independent clustering [18], m”
Multi-Task Multi-View Clustering (2016, methodology): “We use co-clustering as the building block of the framework since it is closely related to both multi-view clustering and The authors are with the Dalian University of Technology, Dalian 116620, China.”
Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, methodology): “The objective function in [124] is defined as”
Multi-view learning overview: Recent progress and new challenges (2017, background): “[97] proposed a multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms.”
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification (2017, methodology): “In particular, the (cid:96) 2 , 1 regulariser performs sparse feature selection for individual image regions as below: (1) We perform feature selective learning at the local region level by enforcing the (cid:96) 1 norm directly on w il,j , conceptually similar to the group LASSO at the global level”