Joint feature selection and optimal bipartite graph learning for subspace clustering.

Recently, there has been tremendous interest in developing graph-based subspace clustering in high-dimensional data, which does not require a priori knowledge of the number of dimensions and subspaces. The general steps of such algorithms are dictionary representation and spectral clustering. Traditional methods use the dataset itself as a dictionary when performing dictionary representation. There are some limitations that the redundant information present in the dictionary and features may make the constructed graph structure unclear and require post-processing to obtain labels. To address these problems, we propose a novel subspace clustering model that first introduces feature selection to process the input data, randomly selects some samples to construct a dictionary to remove redundant information and learns the optimal bipartite graph with K-connected components under the constraint of the (normalized) Laplacian rank. Finally, the labels are obtained directly from the graphs. The experimental results on motion segmentation and face recognition datasets demonstrate the superior effectiveness and stability of our algorithm.