NDLI: Feature selection with stochastic complexity

Content Provider	IEEE Xplore Digital Library
Author	Dom, B. Niblack, W. Sheinvald, J.
Copyright Year	1989
Description	Author affiliation: IBM Almaden Res. Center, San Jose, CA, USA (Dom, B.; Niblack, W.; Sheinvald, J.)
Abstract	The application of J. Rissanen's theory (1986) of stochastic complexity to the problem of features selection in statistical pattern recognition (SPR) is discussed. Stochastic complexity provides a general framework for statistical problems such as coding, prediction, estimation, and classification. A brief review of the SPR paradigm and traditional methods of feature selection is presented, followed by a discussion of the basic of stochastic complexity. Two forms of stochastic complexity, minimum description length and an integral form, are applied to the problem of feature selection. Experimental results using simulated data generated with Gaussian distributions are given and compared with results from cross validation, a traditional technique. The stochastic complexity measures give superior results, as measured by their ability to select subsets of relevant features, as well as probability of error computed based on the selected feature subset.<>
Starting Page	241
Ending Page	248
File Size	704325
Page Count	8
File Format	PDF
ISBN	081861952X
ISSN	10636919
DOI	10.1109/CVPR.1989.37856
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1989-06-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Pattern recognition Training data Jacobian matrices Cognition Length measurement Computational modeling Gaussian distribution Probability Q measurement
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

Local parallel computation of stochastic completion fields

Shape of Gaussians as feature descriptors

Feature selection for multiclass discrimination via mixed-integer linear programming

Circular Regression Based on Gaussian Processes

Learning feature transforms is an easier problem than feature selection

Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression

Project-Out Cascaded Regression with an application to face alignment

Model complexity adaptation using a discriminant measure

Continuous occupancy maps using overlapping local Gaussian processes

Feature selection with stochastic complexity

Similar Documents

Local parallel computation of stochastic completion fields

Shape of Gaussians as feature descriptors

Feature selection for multiclass discrimination via mixed-integer linear programming

Circular Regression Based on Gaussian Processes

Learning feature transforms is an easier problem than feature selection

Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression

Project-Out Cascaded Regression with an application to face alignment

Model complexity adaptation using a discriminant measure

Continuous occupancy maps using overlapping local Gaussian processes

Feature selection with stochastic complexity