NDLI: Study on orthogonal tensor sparse neighborhood preserving embedding algorithm for dimension reduction

Content Provider	IEEE Xplore Digital Library
Author	Mingming Qi Hai Lu Yanqiu Zhang Dongdong Lv Shuhan Yuan Xin Xi
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tongji Univ., Shanghai, China (Yanqiu Zhang; Dongdong Lv; Shuhan Yuan) \|\| Shanghai Minhang District Central Hosp., Shanghai, China (Xin Xi) \|\| Network Center, Tongji Univ., Shanghai, China (Hai Lu) \|\| Sch. of Yuanpei, Shaoxing Univ., Shaoxing, China (Mingming Qi)
Abstract	This paper proposes the orthogonal tensor sparse neighborhood preserving embedding algorithm (OTSNPE) for dimension reduction of the high-dimensional matrix data based on the bag of visual word and in combination with the sparse representation. OTSNPE applies sparse coding to local characteristic quantification of data through completion of within-class sparse representation and preserves the supervised local geometrical information effectively. Finally, the experimental result of the real high-dimensional matrix data set verifies the effectiveness of the algorithm.
Starting Page	1392
Ending Page	1396
File Size	1846018
Page Count	5
File Format	PDF
e-ISBN	9781479969890
DOI	10.1109/WARTIA.2014.6976543
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dimension reduction Tensile stress Tensor Conferences Industry applications Sparse representation Face Mathematical model Sparse matrices Neighborhood preserving embedd-ding Equations
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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