A Fuzzy Least Squares Support Tensor Machines in Machine Learning


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Title A Fuzzy Least Squares Support Tensor Machines in Machine Learning
Creator Zhang, Ruiting
Zhou, Zhijian
Subject Alternating projection; Least square support tensor machines; Support tensor machines; Tensor learning
Description In the machine learning field, high-dimensional data are often encountered in the real applications. Most of the traditional learning algorithms are based on the vector space model, such as SVM. Tensor representation is useful to the over fitting problem in vector-based learning, and tensor-based algorithm requires a smaller set of decision variables as compared to vector-based approaches. We also would require that the meaningful training points must be classified correctly and would not care about some training points like noises whether or not they are classified correctly. To utilize the structural information present in high dimensional features of an object, a tensor-based
learning framework, termed as Fuzzy Least Squares support tensor machine (FLSSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of FLSSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in ORL database and Yale database. The FLSSTM outperforms other tensor-based algorithms, for example,
LSSTM, especially when training size is small.
Publisher kassel university press GmbH
Contributor China Agricultural Research System
Beijing Higher Education Young Elite Teacher Project
Date 2015-12-14
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Identifier https://online-journals.org/index.php/i-jet/article/view/5203
Source International Journal of Emerging Technologies in Learning (iJET); Vol 10, No 8 (2015): Special Issue "Interactive Computer Aided Learning"; pp. 4-10
Language eng
Relation https://online-journals.org/index.php/i-jet/article/view/5203/3690
Rights Copyright (c) 2017 Ruiting Zhang, Zhijian Zhou

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