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Mar 24, 2024 · We propose an Embedded Feature Selection on Graph-Based Multi-View Clustering (EFSGMC) approach to improve the clustering performance.
Our method decomposes anchor graphs, taking advantage of memory efficiency, to obtain clustering labels in a single step without the need for post-processing.
Oct 22, 2024 · Recently, anchor graph-based multi-view clustering has been proven to be highly efficient for large-scale data processing.
Feb 26, 2024 · Our method combines embedding learning and sparse constraint to perform feature selection, allowing us to remove noisy anchor points and redundant connections ...
Specifically, we model the multi-view graph data as tensors and apply tensor factorization to learn the multi-view graph embeddings, thereby capturing the local ...
Feb 23, 2024 · On-demand video platform giving you access to lectures from conferences worldwide.
This paper presents a novel method called One Step Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (OSMGNE).
May 4, 2024 · Our method begins by extracting embedded features from each view ... Graph-based methods typically involve constructing a graph for each view ...
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Nov 1, 2024 · Multi-view clustering techniques, especially spectral clustering methods, are quite popular today in the fields of machine learning and data ...
We propose generating multiple graphs that are as mutually exclusive as possible to enhance the complementarity between views.