Dimensionality Reduction by Canonical Contextual Correlation Projections
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Abstract
A linear, discriminative, supervised technique for reducing feature vec- tors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical Fisher linear discriminant analysis (LDA) and useful, for example, in supervised segmentation tasks in which high-dimensional feature vector describes the local structure of the image. In general, the main idea of the technique is applicable in discriminative and statistical modelling that involves contextual data.
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