A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed.
Abstract. A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is pro ...
(PDF) Dimensionality Reduction by Canonical Contextual ...
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A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed.
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed.
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed.
含まれない: Projections. | 必須にする:Projections.
Dimensionality reduction by canonical contextual correlation projections. In T. Pajdla, & J. Matas (Eds.), ECCV 2004; Proceedings of the eight European ...
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed.
Abstract. We present a fast algorithm for approxi- mate Canonical Correlation Analysis (CCA). Given a pair of tall-and-thin matrices, the.
Dimensionality reduction of image features using the canonical contextual correlation projection. Translated title of the contribution: Dimensionality ...
Abstract. We analyze the multi-view regression problem where we have two views X = (X(1), X(2)) of the input data and a target variable Y of interest.