Proceedings ArticleDOI
Supervised tensor learning
Dacheng Tao,Xuelong Li,Weiming Hu,Stephen J. Maybank,Xindong Wu +4 more
- Vol. 13, Iss: 1, pp 450-457
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TLDR
This paper generalizes MPM to its STL version, which is named the tensor MPM (TMPM), and develops a method for tensor based feature extraction, named the tenor rank-one discriminant analysis (TR1DA).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/sup 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.read more
Citations
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Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
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Geometric Mean for Subspace Selection
TL;DR: Preliminary experimental results show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
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A Comprehensive Survey to Face Hallucination
TL;DR: This paper comprehensively surveys the development of face hallucination, including both face super-resolution and face sketch-photo synthesis techniques, and presents a comparative analysis of representative methods and promising future directions.
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A survey of multilinear subspace learning for tensor data
TL;DR: The central issues of MSL are discussed, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, and examining the algorithmic aspects of typical MSL solutions.
References
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