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In recent years, feature extraction method make an achievement in pattern recognition. It extracts not only useful feature for classification, but also reduces the dimension of pattern sample. Linear discriminant analysis is an important method for image recognition, it achieve significant development both in theory and applications. Local fisher discriminant analysis redefines the between-class and...
In recent years, feature extraction methods make an achievement in pattern recognition and computer vision. It extracts not only useful feature for classification, but also reduces the dimension of pattern samples. In this paper, we propose orthogonal supervised spectral discriminant analysis (OSSDA) which motivated by marginal fisher analysis (MFA) and spectral clustering. It put different weights...
This paper proposes approaches for multi-labeled text categorization based on the boosting algorithms.We discussed the performances of various feature selection methods for multi-labeled TC. Besides the multi-labeled categorization, we also make efforts on ranking the labels assigned to the texts.
Automatic gender classification aims at analyzing the face image to recognize gender with computer, in which feature extraction is one key step. The LBP (local binary pattern) feature has essential applications in face analysis and has been applied in gender recognition. The normally adopted LBP feature will encounter dimension explosion with the increase of sampling density of LBP operator, which...
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