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Regularization and Kernelization of the Maximin Correlation Approach

Title
Regularization and Kernelization of the Maximin Correlation Approach
Authors
Lee, TaehoonMoon, TaesupKim, Seung JeanYoon, Sungroh
DGIST Authors
Moon, Taesup
Issue Date
2016
Citation
IEEE Access, 4, 1385-1392
Type
Article
Article Type
Article
Keywords
Character RecognitionCorrelationCorrelation MethodsIterative MethodsKernel TrickLearning SystemsLinear ProgrammingMaximinNearest NeighborNearest NeighborsOptical Character RecognitionOptimizationQCLPQPRegularizationSOCP
ISSN
2169-3536
Abstract
Robust classification becomes challenging when each class consists of multiple subclasses. Examples include multi-font optical character recognition and automated protein function prediction. In correlation-based nearest-neighbor classification, the maximin correlation approach (MCA) provides the worst-case optimal solution by minimizing the maximum misclassification risk through an iterative procedure. Despite the optimality, the original MCA has drawbacks that have limited its wide applicability in practice. That is, the MCA tends to be sensitive to outliers, cannot effectively handle nonlinearities in datasets, and suffers from having high computational complexity. To address these limitations, we propose an improved solution, named regularized MCA (R-MCA). We first reformulate MCA as a quadratically constrained linear programming (QCLP) problem, incorporate regularization by introducing slack variables in the primal problem of the QCLP, and derive the corresponding Lagrangian dual. The dual formulation enables us to apply the kernel trick to R-MCA, so that it can better handle nonlinearities. Our experimental results demonstrate that the regularization and kernelization make the proposed R-MCA more robust and accurate for various classification tasks than the original MCA. Furthermore, when the data size or dimensionality grows, R-MCA runs substantially faster by solving either the primal or dual (whichever has a smaller variable dimension) of the QCLP. The source code of the proposed R-MCA methodology is available at http://data.snu.ac.kr/rmca. © 2013 IEEE.
URI
http://hdl.handle.net/20.500.11750/5141
DOI
10.1109/ACCESS.2016.2551727
Publisher
Institute of Electrical and Electronics Engineers Inc.
Files:
There are no files associated with this item.
Collection:
Department of Information and Communication EngineeringMachine Learning and Data Science Lab1. Journal Articles


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