NDLI: Spectral error correcting output codes for efficient multiclass recognition

Content Provider	IEEE Xplore Digital Library
Author	Xiao Zhang Lin Liang Heung-Yeung Shum
Copyright Year	2009
Description	Author affiliation: Microsoft Research Asia, Beijing, China (Lin Liang) \|\| Microsoft Corporation, Redmond, WA, USA (Heung-Yeung Shum) \|\| Center for Advanced Study, Tsinghua University, Beijing, China (Xiao Zhang)
Abstract	The error correcting output codes (ECOC) is a general framework to extend any binary classifier to the multiclass case. Finding the optimal ECOC is known as a NP hard problem. In this paper, we present a spectral analysis approach for the design of ECOC. We construct a similarity graph of the classes and generate ECOC with a subset of thresholded eigenvectors of the graph Laplacian. Using the spectral analysis, the coding efficiency, classifier's diversity, Hamming distance among codewords, and binary classifiers' accuracy can be simultaneously considered. The resulting ECOC is efficient, thus only a small set of binary classifiers are to be evaluated when making a decision. In experiments with large multiclass problems, our method is between 3 and 12 times faster comparing to one-against-all, with comparable classification accuracy. Our method also shows a better performance than the most of leading methods, e.g., ClassMap, random dense ECOC, random sparse ECOC, and discriminant ECOC.
Starting Page	1111
Ending Page	1118
File Size	290662
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459355
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Error correction codes Face recognition Matrix decomposition Spectral analysis Laplace equations Computational complexity Asia NP-hard problem Hamming distance Large-scale systems
Content Type	Text
Resource Type	Article
Subject	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

On the Decoding Process in Ternary Error-Correcting Output Codes

Employing Decoding of Specific Error Correcting Codes as a New Classification Criterion in Multiclass Learning Problems

Separability of ternary Error-Correcting Output Codes

Using Hybrid Hadamard Error Correcting Output Codes for Multi-class Problem Based on Support Vector Machines

Subclass Problem-Dependent Design for Error-Correcting Output Codes

ECOC-based training of neural networks for face recognition

Face verification using error correcting output codes

Improved N-division output coding for multiclass learning problems

Efficient decoding of ternary error-correcting output codes for multiclass classification (2009)

Spectral error correcting output codes for efficient multiclass recognition

Similar Documents

On the Decoding Process in Ternary Error-Correcting Output Codes

Employing Decoding of Specific Error Correcting Codes as a New Classification Criterion in Multiclass Learning Problems

Separability of ternary Error-Correcting Output Codes

Using Hybrid Hadamard Error Correcting Output Codes for Multi-class Problem Based on Support Vector Machines

Subclass Problem-Dependent Design for Error-Correcting Output Codes

ECOC-based training of neural networks for face recognition

Face verification using error correcting output codes

Improved N-division output coding for multiclass learning problems

Efficient decoding of ternary error-correcting output codes for multiclass classification (2009)

Spectral error correcting output codes for efficient multiclass recognition