NDLI: Incremental Compressive Pattern Classification

Content Provider	IEEE Xplore Digital Library
Author	Ruta, A.
Copyright Year	2011
Abstract	Compressive sensing (CS) is a technique based on a proven observation that if a signal is compressible, i.e. has a sparse representation in some basis, it can be reconstructed from only a very limited number of measurements. This idea, directly applicable to data compression, has been recently exploited in pattern classification. In this setting an input pattern is projected onto a sparsity basis spanned by a number of other, pre-labeled patterns (a training set), such that expectedly only those representing the same category as the tested pattern are assigned non-zero coefficients. Label of a novel pattern is then determined by generating K signals with attenuated coefficients of the basis patterns not sharing each of K possible labels, and picking the one minimizing the reconstruction error. Our contribution consists in adding recursion to the above scheme. As a result, a newly labeled pattern, if considered "good enough", is added to the training set. The following test patterns are classified in a sparse representation re-calculated under the compressive assumptions from this extended training set. The proposed approach, called "Incremental Compressive Pattern Classification" has been tested on several public image datasets showing superiority to the original algorithm and comparable or better performance than the alternative methods in terms of the classification accuracy.
Starting Page	212
Ending Page	216
File Size	206344
Page Count	5
File Format	PDF
ISBN	9781457721342
DOI	10.1109/ICMLA.2011.78
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training pattern classification Image coding Accuracy Error analysis incremental learning Vectors Compressed sensing Image reconstruction compressive sensing
Content Type	Text
Resource Type	Article

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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
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