Multi-view clustering via multi-manifold regularized non-negative matrix factorization
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TLDR
A multi-manifold regularized non-negative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering.About:
This article is published in Neural Networks.The article was published on 2017-04-01 and is currently open access. It has received 187 citations till now. The article focuses on the topics: Matrix decomposition & Non-negative matrix factorization.read more
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Journal ArticleDOI
Multi-view learning overview
TL;DR: This overview reviews theoretical underpinnings of multi-view learning and attempts to identify promising venues and point out some specific challenges which can hopefully promote further research in this rapidly developing field.
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GMC: Graph-Based Multi-View Clustering
TL;DR: The proposed general Graph-based Multi-view Clustering (GMC) takes the data graph matrices of all views and fuses them to generate a unified graph matrix, which helps partition the data points naturally into the required number of clusters.
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A study of graph-based system for multi-view clustering
TL;DR: A novel multi-view clustering method that works in the GBS framework is also proposed, which can construct data graph matrices effectively, weight each graph matrix automatically, and produce clustering results directly.
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Auto-weighted multi-view clustering via kernelized graph learning
TL;DR: A novel model which simultaneously performs multi-view clustering task and learns similarity relationships in kernel spaces is proposed in this paper, and Experimental results on benchmark datasets demonstrate that the model outperforms other state-of-the-art multi- view clustering algorithms.
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A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE
TL;DR: A multi-task multi-view clustering algorithm in heterogeneous situations based on Locally Linear Embedding and Laplacian Eigenmaps methods (L3E-M2VC), which maps the samples of multiple views from each task to a common view space firstly, then transforms the samples to a discriminative task space secondly, and finally exploits K-Means for clustering.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
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Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
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Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.