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2013 IEEE 13th International Conference on Data Mining (2005)
Houston, Texas
Nov. 27, 2005 to Nov. 30, 2005
ISSN: 1550-4786
ISBN: 0-7695-2278-5
pp: 450-457
Xindong Wu , University of Vermont
Xuelong Li , University of London
Weiming Hu , Chinese Academy of Science
Stephen Maybank , University of London
Dacheng Tao , University of London
ABSTRACT
This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n^th-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.
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CITATION
Xindong Wu, Xuelong Li, Weiming Hu, Stephen Maybank, Dacheng Tao, "Supervised Tensor Learning", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 450-457, 2005, doi:10.1109/ICDM.2005.139
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