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- 2014-02-20
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Description: COST-SENSITIVE SEMI-SUPERVISED DISCRIMINANT ANALYSIS FOR FACE RECOGNITION
Abstract:
In our Project, we present a cost-sensitive semi-supervised discriminant analysis method for face recognition.
In previous methods of dimensionality reduction, they aim to seek low-dimensional feature representations to achieve low classification errors.
In our algorithm, we proposed a new method to learn a discriminative feature subspace by making use of both labeled and unlabeled samples and exploring different cost information of all the training samples simultaneously.
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