Publication | Open Access
L1-Norm-Based 2DPCA
275
Citations
25
References
2010
Year
Experimental ResultsEngineeringData ScienceData MiningPattern RecognitionL1-norm 2DpcaMultidimensional AnalysisMultilinear Subspace LearningBiostatisticsDimensionality ReductionPublic HealthPrincipal Component AnalysisFunctional Data AnalysisLow-rank Approximation
In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.
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