NDLI: Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.

Please wait, while we are loading the content...

Year: 2017 Volume: 28

Year: 2016 Volume: 27

Year: 2016 Volume: 27 Issue: 10

Year: 2016 Volume: 27 Issue: 9

Year: 2016 Volume: 27 Issue: 8

Year: 2016 Volume: 27 Issue: 7

Year: 2016 Volume: 27 Issue: 6

Year: 2016 Volume: 27 Issue: 5

Year: 2016 Volume: 27 Issue: 4

Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms.

Kernel-Based Least Squares Temporal Difference With Gradient Correction.

Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.

Finite-Time Consensus for Multiagent Systems With Cooperative and Antagonistic Interactions.

Sparse LSSVM in Primal Using Cholesky Factorization for Large-Scale Problems.

Extreme Learning Machine for Multilayer Perceptron.

Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines.

Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines.

Robust Gradient Learning With Applications.

An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

Finite-Time Consensus of Multiagent Systems With a Switching Protocol.

Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays.

Effective Discriminative Feature Selection With Nontrivial Solution.

A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels.

Bayesian Robust Tensor Factorization for Incomplete Multiway Data.

Sparse Principal Component Analysis via Rotation and Truncation.

Objective Function and Learning Algorithm for the General Node Fault Situation.

Year: 2016 Volume: 27 Issue: 3

Year: 2016 Volume: 27 Issue: 2

Year: 2016 Volume: 27 Issue: 1

Year: 2015 Volume: 26

Year: 2014 Volume: 25

Year: 2013 Volume: 24

Year: 2012 Volume: 23

Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Lai, Zhihui Wong, Wai Keung Xu, Yong Yang, Jian Zhang, David
Abstract	Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1-norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.
File Format	HTM / HTML
ISSN	2162237X
Issue Number	4
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume Number	27
e-ISSN	21622388
Language	English
Publisher	Institute of Electrical and Electronics Engineeers
Publisher Date	2016-04-01
Publisher Place	United States
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Medical Informatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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