Journal Article10.1016/j.patcog.2021.108412
Incomplete multiview nonnegative representation learning with multiple graphs
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TL;DR: Zhang et al. as mentioned in this paper proposed an effective incomplete multiview nonnegative representation learning (IMNRL) framework, which performs matrix factorization on multiple incomplete graphs and decomposes these incomplete graphs into consensus nonnegative representations and view-specific spectral representations.
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About: This article is published in Pattern Recognition. The article was published on 01 Mar 2022. The article focuses on the topics: Embedding & Cluster analysis.
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Citations
Multiview Jointly Sparse Discriminant Common Subspace Learning
TL;DR: In this paper , a generalized robust multiview discriminant analysis (GRMDA) is proposed to obtain a linear transform for each view and for learning multi-view jointly sparse discriminant common subspace, which can achieve both maximal between-class and minimal within-class variation for data from multiple views in a common space.
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Incremental unsupervised feature selection for dynamic incomplete multi-view data
TL;DR: Wang et al. as mentioned in this paper proposed an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I2MUFS) on incomplete multi-view streaming data, which embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model.
Incomplete Multi-view Learning via Consensus Graph Completion
TL;DR: A novel method, called incomplete multi-view learning via consensus graph completion (IMLCGC), is proposed in this paper, which completes the incomplete graphs based on the consensus among different views and then fuses the completed graphs into a common graph.
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References
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Fast spectral methods for ratio cut partitioning and clustering
L. Hagen,Andrew B. Kahng +1 more
- 01 Jan 1991
TL;DR: It is shown that the second smallest eigenvalue of a matrix derived from the netlist gives a provably good approximation of the optimal ratio cut partition cost.
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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Multiview Consensus Graph Clustering
TL;DR: A multiview consensus clustering method to learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix is proposed.
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Incomplete Multiview Spectral Clustering With Adaptive Graph Learning
TL;DR: The proposed method is the first work that exploits the graph learning and spectral clustering techniques to learn the common representation for incomplete multiview clustering and achieves the best performance in comparison with some state-of-the-art methods.
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