NDLI: Dimensionality reduction by supervised locality analysis

Content Provider	IEEE Xplore Digital Library
Author	Lei Zhang Peipei Peng Xuezhi Xiang Xiantong Zhen
Copyright Year	2015
Description	Author affiliation: Dept. of Med. Biophys., Univ. of Western Ontario, London, ON, Canada (Xiantong Zhen) \|\| Coll. of Inf. & Commun. Eng., Harbin Eng. Univ., Harbin, China (Lei Zhang; Peipei Peng; Xuezhi Xiang)
Abstract	High-dimensional feature representations have recently been widely used for image classification, which not only induce large storage requirement and high computational complexity, but also tend to be lack of discrimination due to redundant and noisy features. In this paper, we propose a novel algorithm named supervised locality analysis (SLA) for dimensionality reduction. In contrast to conventional dimensionality reduction methods, the proposed SLA incorporates supervision into locality analysis by fully exploring multi-class distributions, which can handle the non-linear data structure while preserving intrinsic discriminative information. The obtained compact and highly discriminative features by the SLA is enables more accurate and efficient classification. Moreover, the SLA can be used for supervised dimensionality reduction of both handcrafted and deep learning based features. We have conduced experiments to evaluate the proposed SLA on three datasets for image classification. The SLA has produced state-of-the-art performance and largely outperformed widely-used dimensionality reduction methods.
Starting Page	1488
Ending Page	1492
File Size	723670
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351048
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Principal component analysis Optimization Yttrium Linear programming Manifolds Silicon image classification Dimensionality reduction manifold learning locality analysis
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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