Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation

arXiv:2307.09055v2 Announce Type: replace-cross
Abstract: Recently, tensor low-rank representation (TLRR) has become a popular tool for tensor data recovery and clustering, due to its empirical success and theoretical guarantees. However, existing TLRR methods consider Gaussian or gross sparse noise, inevitably leading to performance degradation when the tensor data are contaminated by outliers or sample-specific corruptions. This paper develops an outlier-robust tensor low-rank representation (OR-TLRR) method that provides outlier detection and tensor data clustering simultaneously based on the t-SVD framework. For …

arxiv clustering cs.cv cs.lg data low outliers representation robust stat.ml tensor type via