Abstract
Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure for novelty. This new method demonstrated a competitive performance on two-dimensional synthetic distributions and on two real-world data sets: handwritten digits and breast-cancer cytology.
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