NDLI: Sparsity-based representation for categorical data

Content Provider	IEEE Xplore Digital Library
Author	Menon, R. Nair, S.S. Srindhya, K. Kaimal, M.R.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Amrita Vishwa Vidyapeetham, Kollam, India (Menon, R.; Nair, S.S.; Srindhya, K.; Kaimal, M.R.)
Abstract	Over the past few decades, many algorithms have been continuously evolving in the area of machine learning. This is an era of big data which is generated by different applications related to various fields like medicine, the World Wide Web, E-learning networking etc. So, we are still in need for more efficient algorithms which are computationally cost effective and thereby producing faster results. Sparse representation of data is one giant leap toward the search for a solution for big data analysis. The focus of our paper is on algorithms for sparsity-based representation of categorical data. For this, we adopt a concept from the image and signal processing domain called dictionary learning. We have successfully implemented its sparse coding stage which gives the sparse representation of data using Orthogonal Matching Pursuit (OMP) algorithms (both Batch and Cholesky based) and its dictionary update stage using the Singular Value Decomposition (SVD). We have also used a preprocessing stage where we represent the categorical dataset using a vector space model based on the TF-IDF weighting scheme. Our paper demonstrates how input data can be decomposed and approximated as a linear combination of minimum number of elementary columns of a dictionary which so formed will be a compact representation of data. Classification or clustering algorithms can now be easily performed based on the generated sparse coded coefficient matrix or based on the dictionary. We also give a comparison of the dictionary learning algorithm when applying different OMP algorithms. The algorithms are analysed and results are demonstrated by synthetic tests and on real data.
Sponsorship	IEEE Kerala Sect.
Starting Page	74
Ending Page	79
File Size	1342368
Page Count	6
File Format	PDF
e-ISBN	9781479921782
DOI	10.1109/RAICS.2013.6745450
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-19
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis SVD Dictionaries Matching pursuit algorithms Vectors Encoding Sparse matrices Matrix decomposition Sparse coding Cholesky decomposition OMP Sparse representation Dictionary learning
Content Type	Text
Resource Type	Article

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

Learning sparse dictionaries with a popularity-based model

Kernel dictionary learning

Dictionary learning by nonnegative matrix factorization with 1/2-norm sparsity constraint

A dictionary learning algorithm for sparse coding by the normalized bilateral projections

Dictionary learning of convolved signals

An adaptive orthogonal matching pursuit algorithm based on redundancy dictionary

Overcomplete Dictionary Design: The Impact of the Sparse Representation Algorithm

K-CPD: Learning of overcomplete dictionaries for tensor sparse coding

BSIK-SVD: A dictionary-learning algorithm for block-sparse representations

Sparsity-based representation for categorical data

Similar Documents

Learning sparse dictionaries with a popularity-based model

Kernel dictionary learning

Dictionary learning by nonnegative matrix factorization with 1/2-norm sparsity constraint

A dictionary learning algorithm for sparse coding by the normalized bilateral projections

Dictionary learning of convolved signals

An adaptive orthogonal matching pursuit algorithm based on redundancy dictionary

Overcomplete Dictionary Design: The Impact of the Sparse Representation Algorithm

K-CPD: Learning of overcomplete dictionaries for tensor sparse coding

BSIK-SVD: A dictionary-learning algorithm for block-sparse representations

Sparsity-based representation for categorical data