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To efficiently deal with the curse of dimensionality in the content-based image retrieval (CBIR) system, a novel image retrieval algorithm is proposed by combination of local discriminant embedding (LDE) and least square SVM (LS-SVM) in this paper. LDE aims to achieve good discriminating performance by integrating the local geometrical structure and class relations between image data. LS-SVM classifier...
Dimensionality reduction algorithms, which try to reduce the dimensionality of data and to enhance the discriminant information, are of paramount importance in document classification. In this paper, a novel dimensionality reduction algorithm called orthogonal locality discriminant embedding (OLDE) is proposed to address these problems. The OLDE algorithm effectively combines the idea of local discriminant...
Face recognition has received growing attention because of its wide applications. In this paper, an efficient face recognition algorithm based on non-negative matrix factorization (NMF) and SVM is proposed. The high dimension face images are first projected into a lower-dimensional subspace using NMF. The SVM classifier is then used to classify the face image into different classes. The experimental...
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