Hierarchical Laplacian Score for unsupervised feature selection
Abstract: In this paper, we address the problem of unsupervised feature selection. This is an important challenge due to the absence of class labels that would guide the search for relevant information. Motivated by this challenge, we define the new method named Hierarchical Laplacian Score (HLS) that constrains the Laplacian Score using a tree topology structure. The purpose of using this structure is to automatically discover local data structure and local nearest neighbors for each data object. Experimental results on various datasets have demonstrated the effectiveness of the proposed algorithm in clustering and classification applications.
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