NDLI: Neighbourhood sensitive preserving embedding for pattern classification

Content Provider	IET Digital Library
Author	Wang, Bing Hui Lin, Chuang Zhao, Xue Feng Lu, Zhe Ming
Abstract	Recently, a large family of supervised or unsupervised manifold learning algorithms that stem from statistical or geometrical theory has been designed to solve the problem of pattern classification. In this study, consider the fact that the data are usually sampled from a low-dimensional manifold space which resides in a high-dimensional Euclidean space, the authors propose a novel two-graph-based supervised linear classification algorithm called neighbourhood sensitive preserving embedding (NSPE). Different from local linear embedding (LLE) (or neighbourhood preserving embedding (NPE)) which preserves the local neighbourhood structure with one graph, NSPE can discover both the intrinsic and discriminant structure of the data manifold by constructing two graphs, that is, the within-class graph and the between-class graph. Thus, the data are mapped into a subspace where the nearby points with the same label are close to each other, whereas the nearby points with different labels are far apart. As a classification method, besides being defined on training samples, NSPE is also defined on testing samples. Experiments carried on the real-world face databases demonstrate that the results of all two-graph-based spectral methods are comparable and better than that of one-graph-based methods.
Starting Page	489
Ending Page	497
Page Count	9
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 8, Aug (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0539
Journal	IET Image Processing
Publisher Date	2014-04-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Between-class Graph Combinatorial Mathematics Computer Vision And Image Processing Technique Data Manifold Discriminant Structure Data Manifold Intrinsic Structure Data Sampling Face Recognition Graph Theory High-dimensional Euclidean Space Image Classification Image Recognition Learning in AI LLE Locally Linear Embedding Low-dimensional Manifold Space Neighbourhood Sensitive Preserving Embedding NPE NSPE One-graph-based Method Pattern Classification Sampling Method Statistics Two-graph-based Spectral Method Two-graph-based Supervised Linear Classiflcation Algorithm Within-class Graph
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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

Supervised sparse neighbourhood preserving embedding

Two-dimensional neighborhood preserving embedding for face recognition

Directional Two-dimensional Neighborhood Preserving Projection for Face Recognition

Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs

Learning a nonlinear embedding by preserving class neighbourhood structure.

Feature Extraction Based on Class Mean Embedding (CME)

Document categorization algorithm based on kernel NPE

Head pose estimation and face recognition using a non-linear tensor-based model

Point pattern matching based on manifold embedding

Neighbourhood sensitive preserving embedding for pattern classification

Similar Documents

Supervised sparse neighbourhood preserving embedding

Two-dimensional neighborhood preserving embedding for face recognition

Directional Two-dimensional Neighborhood Preserving Projection for Face Recognition

Hyperspectral image classification based on adaptive-weighted LLE and clustering-based FSVMs

Learning a nonlinear embedding by preserving class neighbourhood structure.

Feature Extraction Based on Class Mean Embedding (CME)

Document categorization algorithm based on kernel NPE

Head pose estimation and face recognition using a non-linear tensor-based model

Point pattern matching based on manifold embedding

Neighbourhood sensitive preserving embedding for pattern classification