Conference Proceedings

Mining labelled tensors by discovering both their common and discriminative subspaces

W Liu, J Chan, J Bailey, C Leckie, K Ramamohanarao

Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 | Published : 2013

Abstract

Copyright © SIAM. Conventional non-negative tensor factorization (NTF) methods assume there is only one tensor that needs to be decomposed to low-rank factors. However, in practice data are usually generated from different time periods or by different class labels, which are represented by a sequence of multiple tensors associated with different labels. This raises the problem that when one needs to analyze and compare multiple tensors, existing NTF is unsuitable for discovering all potentially useful patterns: 1) if one factorizes each tensor separately, the common information shared by the tensors is lost in the factors, and 2) if one concatenates these tensors together and forms a larger ..

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