NDLI: Discriminative components of data

Content Provider	IEEE Xplore Digital Library
Author	Peltonen, J. Kaski, S.
Copyright Year	1990
Abstract	A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.
Sponsorship	IEEE Computational Intelligence Society
Page Count	16
File Size	643260
Starting Page	68
Ending Page	83
File Format	PDF
ISSN	10459227
Volume Number	16
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mutual information Linear discriminant analysis Entropy Data visualization Predictive models Computational efficiency Data analysis Covariance matrix Neural networks Information analysis mutual information Component model discriminant analysis exploratory data analysis learning metrics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications 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

Visualizing class structure in data using mutual information

Discriminant Independent Component Analysis

Visualization and self-organization of multidimensional data through equalized orthogonal mapping

Maximization of Mutual Information for Supervised Linear Feature Extraction

Pruning Noisy Bases in Discriminant Analysis

Recursive training of neural networks for classification

Discriminant analysis neural networks

Artificial neural networks for feature extraction and multivariate data projection

Interactive visualization and analysis of hierarchical neural projections for data mining

Discriminative components of data

Similar Documents

Visualizing class structure in data using mutual information

Discriminant Independent Component Analysis

Visualization and self-organization of multidimensional data through equalized orthogonal mapping

Maximization of Mutual Information for Supervised Linear Feature Extraction

Pruning Noisy Bases in Discriminant Analysis

Recursive training of neural networks for classification

Discriminant analysis neural networks

Artificial neural networks for feature extraction and multivariate data projection

Interactive visualization and analysis of hierarchical neural projections for data mining

Discriminative components of data