Unsupervised Non-redundant Feature Selection: A Graph-Theoretic Approach
In this article a graph-theoretic approach for non-redundant unsupervised feature selection has been presented. The input data matrix is first converted into a weighted undirected complete feature-graph where the nodes represent the features and the edges are weighted according to the dissimilarity of features. Then the densest subgraph having maximum average weight is identified from the original feature graph. The features contained in the reduced subgraph are the final selected features for which average correlation is very less. The proposed method is compared with other dimensionality reduction techniques such as SFS and SBS in terms of entropy, classification accuracy, class separability, average correlation and execution time on several real life data sets.
KeywordsFilter Unsupervised Entropy Class Separability
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