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Linear representations are widely used to reduce dimension in applications involving high dimensional data. While specialized procedures exist for certain optimality criteria, such as principle component analysis (PCA) and Fisher discrim-inant analysis (FDA), they can not be generalized for more general criteria. To overcome this fundamental limitation,(More)
To develop a computer-aided diagnostic scheme of the CT in the diagnosis of lung cancer based on artificial neural networks (ANN) to assist radiologists in distinguishing malignant from benign pulmonary nodules. 117 CT images of pulmonary nodules (58 benign and 59 malignant) were analyzed. 21 CT radiological features of each case were carefully selected and(More)
In this paper, a discriminative manifold learning method for face recognition is proposed which achieved the dis-criminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the recently proposed LLE, Isomap and Eigenmap algorithms, which are based on reconstruction purpose, our method use the RCA algorithm to achieve(More)
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. Optimal component analysis (OCA) formulates the problem in the framework of optimization on a Grassmann manifold and a stochastic gradient method is used to estimate the optimal basis. OCA has been(More)