Graph regularized low-rank tensor representation for feature selection

Recently, considerable efforts have been made in feature selection to improve the original feature subspace. In this paper, we proposed a graph regularized low-rank tensor representation (GRLTR) for feature selection. We jointly incorporated the low-rank representation and the graph embedding into a unified learning framework to preserve the intrinsic global low-dimension structure and local geometrical structure of data together. According to the wide presence of multidimensional data, our proposed framework is based on tensor, which can faithfully maintain the information. To improve the performance of specific clustering task, we employed the idea of embedded-based feature selection into our model for optimizing the feature representation and clustering result simultaneously. Experimental results on six available datasets suggest our proposed approach produces superior performances compared with several state-of-the-art methods.