Application of LVQ to novelty detection using outlier training data

Authors:
Hyoung-joo Lee;Sungzoon Cho
Affiliations:
Department of Industrial Engineering, Seoul National University, San 56-1, Shillim-dong, Kwanak-gu, Seoul 151-744, Republic of Korea;Department of Industrial Engineering, Seoul National University, San 56-1, Shillim-dong, Kwanak-gu, Seoul 151-744, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

We propose to use learning vector quantization (LVQ) in novelty detection where a few outliers exist in training data. The codebook update of original LVQ is modified and the scheme to determine a threshold for each codebook is proposed. Experimental results on artificial and real-world problems are quite promising.