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An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the...
This paper provides a new approach to reconstruct a fluid field from sparse sensor observations. Using the extreme learning machine (ELM) autoencoder, we can extract a dominant basis of the fluid field of interest from a database consisting of a series of fluid field snapshots obtained from offline computational fluid dynamics (CFD) simulations. The output weights of ELM autoencoder can be viewed...
In this paper, we propose a novel subspace learning algorithm, termed as null space based discriminant sparse representation large margin (NDSLM). There are two contributions in the paper. First, we propose a new expectation to obtain the neighborhood information for large margin subspace learning, i.e., the within-neighborhood scatter and betweenneighborhood scatter are modeled by the sparse reconstruction...
In this paper, we consider a linear supervised dimension reduction method for classification settings: Stochastic Discriminant Analysis. This method matches point similarities in the projection space with those in a response space. These similarities are represented by t-distributed joint pairwise probabilities. The matching is done by minimizing the Kullback-Leibler divergence between the two probability...
This paper introduces a novel approach to examine the scope of touch perception as a possible modality of treatment of patients suffering from certain mental disorder using a Radial Basis function induced Back Propagation Neural Network. Experiments are designed to understand the perceptual difference of schizophrenic patients from normal and healthy subjects with respect to four different touch classes,...
A number of unsupervised learning algorithms seeking to account for the receptive field properties of simple cells in the mammalian primary visual cortex have been proposed. Among these are principal component analysis and sparse coding. While it appears that the receptive field properties learned by sparse coding match those measured in cortical cells better than those learned by principal component...
Conditional nonlinear optimal perturbation (CNOP) is an extension of linear singular vector(LSV) to nonlinear optimization. Generally, CNOP is solved with such adjoint based algorithms as SPG2, SQP. Unfortunately, it is often difficult to obtain the corresponding adjoint models for some nonlinear models. In addition, for nonlinear models containing discontinuous “on-off” switches, the adjoint based...
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