NDLI: Matrixized Learning Machine with Feature-Clustering Interpolation

Content Provider	Springer Nature Link
Author	Zhu, Yujin Wang, Zhe Gao, Daqi
Copyright Year	2015
Abstract	The existing matrixized learning machines (MLMs) use bilateral weight vectors on both side of one pattern as the constraints to manipulate matrix-based datasets directly. However, MLM might be challenged while the input pattern is a vector whose features are independent from each others. The traditional solution is to transform the vector into its all corresponding matrix forms, which is not only irrational, but also requiring extra computation in preprocessing. To overcome the problem, this paper proposes a novel matrixized learning model that utilizes an efficient clustering-based interpolation strategy to mapping the original vector-based pattern to an unique matrix. The proposed method first selects the most typical features defined as $$candidate\hbox {s}$$ from each dimension of all patterns in the same class through a fast clustering method, and then measures the relationship between the features of each training pattern and the $$candidate\hbox {s}$$ to generate a new matrix named the candidate-matrix in turn. Afterwards, the candidate-matrix is combined with the pattern to form the corresponding final matrix. At last, all final matrices are collected to form the new training set for the subsequent matrixized classifier. Named FCIMLM for short, the proposed matrixized method is proved more effective and efficient than the traditional MLM under the same structural risk minimum framework through the designed experiments on 21 vector-based benchmark datasets from UCI repository. The main contributions of this paper are: (1) proposing a new matrixized learning model with a more efficient matrixization process using a feature-based fast clustering strategy; (2) combining the feature-clustering-based interpolation to the matrix-pattern-oriented classifier for the first time; (3) extending the existing MLM design techniques.
Starting Page	291
Ending Page	306
Page Count	16
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	44
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2015-07-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Matrixized classifier Interpolation mapping Fast clustering Pattern recognition Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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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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