Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data

Understand

A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper.

• Instead of reshaping multi-way data into vectors, this method maintains their natural orders to preserve data intrinsic structures, e.g., image data kept as matrices.
• To implement clustering, the multi-way data, viewed as tensors, are represented by the proposed tensor sparse and low-rank model to obtain its submodule representation, called a free module, which is finally used for spectral clustering.
• The proposed method extends the conventional subspace clustering method based on sparse and low-rank representation to multi-way data submodule clustering by combining t-product operator.