Piecewise linear dimension reduction for nonnegative data

Bin Shen; Qifan Wang; Jan P. Allebach

doi:10.1117/12.2085391

6 March 2015 Piecewise linear dimension reduction for nonnegative data

Bin Shen, Qifan Wang, Jan P. Allebach

Proceedings Volume 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015; 940804 (2015) https://doi.org/10.1117/12.2085391
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

In past decade, the increasing popularity of imaging devices, especially smart phones, has led to a great increase in the amount of visual data. The rapidly increasing large scale data pose challenges to the storage and computational resources, and make many computer vision and pattern recognition tasks prohibitively expensive. Dimension reduction techniques explore hidden structures of the original high dimensional data and learn new low dimensional representation to alleviate the challenges. Popular dimension reduction techniques, such as PCA and NMF, do an efficient linear mapping to low dimensional space, while nonlinear techniques overcomes the limitation of linearity at the cost of expensive computational cost (e.g. computing the pairwise distance to find the geodesic distance). In this paper, a piecewise linear dimension reduction technique with global consistency and smoothness constraint is proposed to overcome the restriction of linearity at relatively low cost. Extensive experimental results show that the proposed methods outperform the linear method in the scenario of clustering both consistently and significantly.

Citation Download Citation

Bin Shen, Qifan Wang, and Jan P. Allebach "Piecewise linear dimension reduction for nonnegative data", Proc. SPIE 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015, 940804 (6 March 2015); https://doi.org/10.1117/12.2085391

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available