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One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is(More)
In this paper, we propose an efficient algorithm to reconstruct the 3D structure of a human face from one or more of its 2D images with different poses. In our algorithm, the nonlinear least-squares model is first employed to estimate the depth values of facial feature points and the pose of the 2D face image concerned by means of the similarity transform.(More)
This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are(More)
A novel neural network technique for nonnegative independent component analysis is proposed in this letter. Compared with other algorithms, this method can work efficiently even when the source signals are not well grounded. Moreover, this method is insensitive to the particular underlying distribution of the source data. Experimental results demonstrate(More)
In this paper, we propose a novel and efficient algorithm to reconstruct the 3-D structure of a human face from one or a number of its 2-D images with different poses. In our proposed algorithm, the rotation and translation process from a frontal-view face image to a nonfrontal-view face image is at first formulated as a constrained independent component(More)
In this letter, a two-step learning scheme for the optimal selection of time lags is proposed for a typical temporal blind source separation (TBSS), Temporal Decorrelation source SEParation algorithm (abbreviated as TDSEP). Given the time lags, the time-delayed second-order correlation matrices are first diagonalized simultaneously. Then, a genetic(More)
In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of(More)