Structured graph learning for clustering and semi-supervised classification
TLDR
This paper proposes a graph learning framework to preserve both the local and global structure of data that uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure.About:
This article is published in Pattern Recognition.The article was published on 2021-02-01 and is currently open access. It has received 89 citations till now. The article focuses on the topics: Connected component & Cluster analysis.read more
Citations
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Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview
TL;DR: Wang et al. as mentioned in this paper proposed a scalable graph learning framework, which is based on the ideas of anchor points and bipartite graph, to solve the problems of expensive time overhead, inability to explore the explicit clusters, and cannot generalize to unseen data points.
Journal ArticleDOI
Pseudo-supervised Deep Subspace Clustering
TL;DR: Zhang et al. as discussed by the authors used pairwise similarity to weigh the reconstruction loss to capture local structure information, while a similarity is learned by the self-expression layer, which can address the large-scale and out-of-sample problems.
Journal ArticleDOI
Multi-view subspace clustering via partition fusion
TL;DR: In this article, the authors propose to fuse multi-view information in a partition space, which enhances the robustness of multiview clustering by generating multiple partitions and integrating them to find a shared partition.
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