In this paper, Ganetespib structure we present an assumption that is consistent with the viewpoint of discrimination, that is, in the same feature space, one person’s different biometric identifier data can form different Gaussians, and thus his overall biometric data can be described using mixture-Gaussian models. Although LDA has been widely used in biometrics to extract discriminative features, it has the limits that it can only handle the data of one person that forms a single Gaussian distribution. However, as we pointed out above, in multimodal analysis, different biometric identifier data of one person can form mixture-Gaussians. Fortunately, subclass discriminant analysis (SDA) [30] has been proposed to remove such a limit of LDA, and therefore could be used to describe multimodal data that lie in the same input space.
Based on the analysis above, in this paper we propose a novel multimodal biometric data feature extraction scheme based on subclass discriminant analysis (SDA) [20]. For simplicity, we consider two typical types of biometric data, that is, face data and palmprint data. For Inhibitors,Modulators,Libraries one person, his face data and palmprint Inhibitors,Modulators,Libraries data are regarded as two subclasses of one class, and discriminative features are extracted by seeking an embedded space, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, since the parallel fusion strategy is not suitable to fuse features from multiple modals, we fuse the obtained features by adopting the serial fusion strategy Inhibitors,Modulators,Libraries and use them for classification.
Two solutions are presented to solve the small sample size problem encountered in calculating the optimal transform. One is to initially do PCA preprocessing, and the other is to employ the generalized singular value decomposition (GSVD) [31,32] technique. Moreover, it is still worthy to explore the non-linear Inhibitors,Modulators,Libraries discriminant capability of SDA in multimodal feature fusion, in particular, when some single-modals still show complicated and non-linearly separable data distribution. Hence, in this paper, we further extend SDA feature fusion approach in the kernel space and present two solutions to solve the small sample size problem, which are KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first use KPCA to transform each single modal input space Rn into an m-dimensional space, where m = rank(K), K is the centralized Gram matrix.
Then Drug_discovery SDA is used to fuse the two transformed kinase inhibitor Afatinib features and extract discriminative features. In KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem.We evaluate the proposed approaches on two face databases (AR and FRGC), and the PolyU palmprint database, and compare the results with related methods that also tend to extract descriptive features from multimodal data.
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