NDLI: Introducing Subspace Grids to Recognise Patterns in Multidimensinal Data

Content Provider	IEEE Xplore Digital Library
Author	Wani, M.A.
Copyright Year	2012
Abstract	The work presented in this paper proposes a new approach of using subspace grids for recognizing patterns in multidimensional data. The proposed approach addresses the two problems often associated with this task: i) curse of dimensionality ii) cases with small sample sizes. To handle the curse of dimensionality problem, this paper introduces subspace grids and shows how it can be employed for pattern recognition tasks efficiently. To address the cases with small sample sizes, this paper proposes a multi-scale approach where coarse scale, being stable and generic in nature, suits well for small sample sizes, and fine scales, being more specialized in nature, enhance classification accuracy. The paper first describes projection of multidimensional data to a number of lower dimensional subspaces. Principal component analysis (PCA) and multiple discriminant analysis (MDA) algorithms are used to define lower dimensional subspaces. The range of value associated with each vector of a subspace is divided into a number of equal parts to define coarse subspace grids. Coarse subspace grids are further divided equally into fine subspace grids. A recursive procedure is then employed to obtain rules where coarse and fine subspace grids form premises of rules. The system is tested on the bench mark IRIS data set having 150 examples. (50 examples belonging to each class type). The results show that the use of subspaces grids produces good results to recognize patterns in multidimensional data.
Starting Page	33
Ending Page	39
File Size	385508
Page Count	7
File Format	PDF
ISBN	9781467346511
DOI	10.1109/ICMLA.2012.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis subspace grids multi-scale approach Vectors multiple discriminant analysis Pattern recognition Classification algorithms machine learning Accuracy principle component analysis Feature extraction rule extraction Principal component analysis pattern recognition
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
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@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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