Recently, multi-view high-dimensional data has drawn great attention due to its reflection of different properties or distributions and multi-view clustering have proven to be one of the most efficient approaches in the high-dimensional data processing. However, the existing graph-based clustering methods have high computational complexity and are difficult to obtain effective information when dealing with high-dimensional data. To solve this problem, we propose \underline{\textbf{E}}mbedded \underline{\textbf{F}}eature \underline{\textbf{S}}election on \underline{\textbf{G}}raph-based \underline{\textbf{M}}ulti-view \underline{\textbf{C}}lustering (EFSGMC) to enhance the clustering performance. Our model performs feature selection on the anchor graph with memory advantage to select a subset of the most relevant features. In addition, we use dispersed cluster assignment matrix to obtain cluster labels without post-processing. Finally, we applied the tensor Schatten $p$-norm as a tensor rank approximation function to obtain the complementary information between different views to ensure the similarity between the cluster assignment matrices. Experimental results on five real datasets demonstrate that our proposed method is superior to the state-of-the-art methods.

Embedded Feature Selection on Graph-Based Multi-View Clustering

Main Track - Machine Learning

technical paper

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Guangfei Li

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Embedded Feature Selection on Graph-Based Multi-View Clustering

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