Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
Abstract
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. The new method is tested on several public datasets, including synthetical data, video sequences and toy images. The experiments show that the new method outperforms the state-of-the-art methods, such as Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR), Ordered Subspace Clustering (OSC), Robust Latent Low Rank Representation (RobustLatLRR) and Sparse Submodule Clustering method (SSmC).
Cite
@article{arxiv.1601.00149,
title = {Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data},
author = {Xinglin Piao and Yongli Hu and Junbin Gao and Yanfeng Sun and Zhouchen Lin and Baocai Yin},
journal= {arXiv preprint arXiv:1601.00149},
year = {2016}
}
Comments
We want to withdraw this paper because we need more mathematical derivation and experiments to support our method. Therefore, we think this paper is not suitable to be published in this period