Multilinear Subspace Clustering
This work addresses image clustering for computer vision applications, but it is incremental as it builds on the established union of subspaces model with a structured variant.
The paper tackles unsupervised clustering of 2-D data like images by proposing a Multilinear Subspace Clustering (MSC) algorithm based on a union of multilinear subspaces model, showing it is highly competitive in clustering performance on YaleB and Olivietti datasets while improving computational complexity.
In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images. We assume that the data comes from a union of multilinear subspaces (UOMS) model, which is a specific structured case of the much studied union of subspaces (UOS) model. For segmentation under this model, we develop Multilinear Subspace Clustering (MSC) algorithm and evaluate its performance on the YaleB and Olivietti image data sets. We show that MSC is highly competitive with existing algorithms employing the UOS model in terms of clustering performance while enjoying improvement in computational complexity.