Coupled-Feature Hypergraph Representation for Feature Selection
Real-world objects and their features tend to exhibit multiple relationships rather than simple pairwise ones, and as a result basic graph representation can lead to substantial loss of information. Hypergraph representations, on the other hand, allow vertices to be multiply connected by hyperedges and can hence capture multiple or higher order relationships among features. Due to their effectiveness in representing multiple relationships, in this paper, we draw on recent work on hypergraph clustering to select the relevant feature subset (RFS) from a set of features using high-order (rather than pairwise) similarities. Specifically, we first devise a coupled feature representation to represent the data by utilizing self-coupled and inter-feature coupling relationships, which can be more effective to capture the intrinsic linear and nonlinear information on data structure. Regarding the new data representation, we use a new information theoretic criterion referred to as multivariate mutual information to measure the high-order feature combinations with respect to the class labels. Therefore, we construct a coupled feature hypergraph to model the high-order relations among features. Finally, we locate the relevant feature subset (RFS) from feature hypergraph by maximizing features’ average relevance, which has both low redundancy and strong discriminating power. The size of the relevant feature subset (RFS) is determined automatically. Experimental results demonstrate the effectiveness of our feature selection method on a number of standard data-sets.
KeywordsHypergraph Coupled feature analysis Feature selection
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