Add to ORKGAutoassociator is an important issue in concept learn-ing, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoassociation, this paper presents a new model referred to as kernel autoassociator. Using kernel feature space as a potential nonlinear manifold, the model formulates the au-toassociation as a special reconstruction problem from ker-nel feature space to input space. Two methods are devel-oped to solve the problem. We evaluate the autoassociator with artificial data, and apply it to handwritten digit recog-nition and multiview face recognition, yielding positive ex-perimental results. 1
Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recogni...
This paper presents an autoassociator neural network for texture feature extraction. Texture feature...
Abstract—Scene images typically include diverse and distinc-tive properties. It is reasonable to con...
Abstract—Autoassociators are a special type of neural networks which, by learning to reproduce a giv...
10.1109/ICPR.2004.1334252Proceedings - International Conference on Pattern Recognition2443-446PICR
Using autoassociativity principle, local connections, weight sharing, and proximity of input pixels,...
Recent researches have shown the necessity to consider multiple kernels rather than a single fixed k...
Face images are subject to changes in view and illumination. Such changes cause data distribution to...
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinea...
International audienceA major issue in statistical machine learning is the design of a representa-ti...
10.1109/TSMCB.2005.843980IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics3535...
Recent research has shown that collaborative representation-based classifier (CRC) can lead to promi...
This paper is organized as follows: after presenting a brief description of a face autoassociative m...
A kernel principal component analysis (PCA) was recently proposed as a nonlinear extension of a PCA....
Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recogni...
Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recogni...
This paper presents an autoassociator neural network for texture feature extraction. Texture feature...
Abstract—Scene images typically include diverse and distinc-tive properties. It is reasonable to con...
Abstract—Autoassociators are a special type of neural networks which, by learning to reproduce a giv...
10.1109/ICPR.2004.1334252Proceedings - International Conference on Pattern Recognition2443-446PICR
Using autoassociativity principle, local connections, weight sharing, and proximity of input pixels,...
Recent researches have shown the necessity to consider multiple kernels rather than a single fixed k...
Face images are subject to changes in view and illumination. Such changes cause data distribution to...
Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinea...
International audienceA major issue in statistical machine learning is the design of a representa-ti...
10.1109/TSMCB.2005.843980IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics3535...
Recent research has shown that collaborative representation-based classifier (CRC) can lead to promi...
This paper is organized as follows: after presenting a brief description of a face autoassociative m...
A kernel principal component analysis (PCA) was recently proposed as a nonlinear extension of a PCA....
Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recogni...
Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recogni...
This paper presents an autoassociator neural network for texture feature extraction. Texture feature...
Abstract—Scene images typically include diverse and distinc-tive properties. It is reasonable to con...