NDLI: Sparse Unsupervised Dimensionality Reduction for Multiple View Data

Content Provider	IEEE Xplore Digital Library
Author	Yahong Han Fei Wu Dacheng Tao Jian Shao Yueting Zhuang Jianmin Jiang
Copyright Year	1991
Abstract	Different kinds of high-dimensional visual features can be extracted from a single image. Images can thus be treated as multiple view data when taking each type of extracted high-dimensional visual feature as a particular understanding of images. In this paper, we propose a framework of sparse unsupervised dimensionality reduction for multiple view data. The goal of our framework is to find a low-dimensional optimal consensus representation from multiple heterogeneous features by multiview learning. In this framework, we first learn low-dimensional patterns individually from each view, considering the specific statistical property of each view. We construct a low-dimensional optimal consensus representation from those learned patterns, the goal of which is to leverage the complementary nature of the multiple views. We formulate the construction of the low-dimensional consensus representation to approximate the matrix of patterns by means of a low-dimensional consensus base matrix and a loading matrix. To select the most discriminative features for the spectral embedding of multiple views, we propose to add an l₁-norm into the loading matrix's columns and impose orthogonal constraints on the base matrix. We develop a new alternating algorithm, i.e., spectral sparse multiview embedding, to efficiently obtain the solution. Each row of the loading matrix encodes structured information corresponding to multiple patterns. In order to gain flexibility in sharing information across subsets of the views, we impose a novel structured sparsity-inducing norm penalty on the loading matrix's rows. This penalty makes the loading coefficients adaptively load shared information across subsets of the learned patterns. We call this method structured sparse multiview dimensionality reduction. Experiments on a toy benchmark image data set and two real-world Web image data sets demonstrate the effectiveness of the proposed algorithms.
Sponsorship	IEEE Circuits and Systems Society
Starting Page	1485
Ending Page	1496
Page Count	12
File Size	10690738
File Format	PDF
ISSN	10518215
Volume Number	22
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Loading Vectors Feature extraction Principal component analysis Visualization Educational institutions video and image classification Multiple view data structured sparsity
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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