Multiview spectral clustering via structured low-rank matrix factorization
Multiview data clustering attracts more attention than their single-view counterparts due to
the fact that leveraging multiple independent and complementary information from multiview …
the fact that leveraging multiple independent and complementary information from multiview …
Feature selection based on structured sparsity: A comprehensive study
Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …
Multi-view intact space learning
It is practical to assume that an individual view is unlikely to be sufficient for effective multi-
view learning. Therefore, integration of multi-view information is both valuable and …
view learning. Therefore, integration of multi-view information is both valuable and …
Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data
objects grouping across multi-views with their graph laplacian matrices, is a fundamental …
objects grouping across multi-views with their graph laplacian matrices, is a fundamental …
Generalized uncorrelated regression with adaptive graph for unsupervised feature selection
Unsupervised feature selection always occupies a key position as a preprocessing in the
tasks of classification or clustering due to the existence of extra essential features within high …
tasks of classification or clustering due to the existence of extra essential features within high …
Tensor canonical correlation analysis for multi-view dimension reduction
Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension
reduction due to its profound theoretical foundation and success in practical applications. In …
reduction due to its profound theoretical foundation and success in practical applications. In …
Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion
Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature
representations may combat this problem from different aspects; as visual data objects …
representations may combat this problem from different aspects; as visual data objects …
Discriminative subspace matrix factorization for multiview data clustering
In a real-world scenario, an object is easily considered as features combined by multiple
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
Multiview matrix completion for multilabel image classification
There is growing interest in multilabel image classification due to its critical role in web-
based image analytics-based applications, such as large-scale image retrieval and …
based image analytics-based applications, such as large-scale image retrieval and …
Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering
Y Wang, L Wu - Neural Networks, 2018 - Elsevier
Abstract Low-Rank Representation (LRR) is arguably one of the most powerful paradigms
for Multi-view spectral clustering, which elegantly encodes the multi-view local …
for Multi-view spectral clustering, which elegantly encodes the multi-view local …