DocumentCode :
116050
Title :
Linear regression for pattern recognition
Author :
Stephen, Priya ; Jaganathan, Suresh
Author_Institution :
Dept. of Comput. Sci. & Eng, Sri Sivasubramaniya Nadar Coll. of Eng., Chennai, India
fYear :
2014
fDate :
6-8 March 2014
Firstpage :
1
Lastpage :
6
Abstract :
This paper presents a novel method for pattern recognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model representing a probe image as a linear combination of class-specific galleries. Linear Regression Classification (LRC) algorithm for pattern recognition belongs to the category of nearest subspace classification. This algorithm is extensively evaluated on several standard digit and English character databases and our own Tamil character database. A comparative study with different databases and methods clearly reflects the efficiency of LRC approach for pattern recognition.
Keywords :
handwritten character recognition; pattern classification; regression analysis; English character databases; LRC approach; Tamil character database; class-specific galleries; linear regression classification algorithm; linear subspace; pattern recognition; probe image; single-object class; standard digit databases; Databases; Linear regression; Mathematical model; Pattern recognition; Testing; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on
Conference_Location :
Coimbatore
Type :
conf
DOI :
10.1109/ICGCCEE.2014.6921393
Filename :
6921393
Link To Document :
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