A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Generalized Low Rank Approximations of Matrices
Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Decompositions and Applications
SIAM Review
Approximation algorithms for tensor clustering
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust principal component analysis with non-greedy l1-norm maximization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology
Tensor Learning for Regression
IEEE Transactions on Image Processing
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Tensors are increasingly common in several areas such as data mining, computer graphics, and computer vision. Tensor clustering is a fundamental tool for data analysis and pattern discovery. However, there usually exist outlying data points in real-world datasets, which will reduce the performance of clustering. This motivates us to develop a tensor clustering algorithm that is robust to the outliers. In this paper, we propose an algorithm of Robust Tensor Clustering (RTC). The RTC firstly finds a lower rank approximation of the original tensor data using a L1 norm optimization function. Because the L1 norm doesn't exaggerate the effect of outliers compared with L2 norm, the minimization of the L1 norm approximation function makes RTC robust to outliers. Then we compute the HOSVD decomposition of this approximate tensor to obtain the final clustering results. Different from the traditional algorithm solving the approximation function with a greedy strategy, we utilize a non-greedy strategy to obtain a better solution. Experiments demonstrate that RTC has better performance than the state-of-the-art algorithms and is more robust to outliers.