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At present, it is a great challenge that solving high-dimension and text sparsity problems in short text classification. To resolve these problems, this paper proposes a method which takes the correlation between lexical items and tags before completing Latent Dirichlet Allocation(LDA) topic model. Meanwhile, this paper adjusts parameters of Support Vector Machine(SVM) to find the optimal values by...
Face recognition is a quintessential biometric technique. It still remains challenging to accurately characterize the identity related features in face images. In this paper, we propose a novel classification method based on Kernel Fisher Discriminant Analysis using the distinctiveness of Gabor features and the robustness of ordinal measures. These parameters are derived from magnitude, phase, real...
This research aims at studying the recognition accuracy and execution time that are affected by different dimensionality reduction methods applied to the biometric image data. We comparatively study the fingerprint, face images, and handwritten signature data that are pre-processed with the two statistical based dimensionality reduction methods: principal component analysis (PCA) and linear discriminant...
To address the problem of “curse of dimensionality”, usually dimensionality reduction is used to reduce data's dimensionalities. As a graph-based method for linear dimensionality reduction, Locality Preserving Projections (LPP) searches for an embedding space in which the similarity among the local neighborhoods is preserved. However, LPP has two disadvantages: Firstly, LPP doesn't take the label...
This study presents a physiological recognition strategy based on HRV-parameter-based recognition strategy. The strategy consists of the following processes: 1) feature generation, 2) feature selection, 3) feature extraction, and 4) classifier construction for recognition. In the feature generation processes, the parameter-based strategy calculates features from five-minute HRV analysis results. In...
The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. How to fast and efficiently annotate the type of an uncharacterized membrane protein is a challenge. Some discrete models, such as DC (dipeptide composition) have been proposed to represent a protein sequence in the field of predicting membrane protein types...
Although various discriminant analysis approaches have been used in content-based image retrieval (CBIR) application, there have been relatively few concerns with kernel-based methods. Furthermore, these CBIR applications still applied discriminant analysis to face images as face recognition did. In this paper we concerns images with general semantic concepts. We use our presented symmetrical invariant...
This paper presents a novel face recognition method based on the contourlet for facial features representation and using an new kernel based algorithm, for discriminating purposes, namely kernel relevance weighted discriminant analysis (KRWDA). This nonlinear reduction dimension algorithm has several interesting characteristics. First, using kernel theory, it handles nonlinearity efficiently. Second,...
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