Modeling local nonlinear correlations using subspace principal curves

A signal subspace approach for modeling the hemodynamic response function in fMRI.

Many fMRI analysis methods use a model for the hemodynamic response function (HRF). Common models of the HRF, such as the Gaussian or Gamma functions, have parameters that are usually selected a priori by the data analyst. A new method is presented that characterizes the HRF over a wide range of parameters via three basis signals derived using principal component analysis (PCA). Covering the HR...

Latent Subspace Clustering based on Deep Neural Networks

Clustering approaches have been widely used in process control community for unsupervised classification beneficial for further analysis, modeling and optimization. Process data generally involve far more dimensions than needed; this phenomenon is called as ”data rich but information poor” and becomes obstacles for reasonable classification. Therefore, it is desirable to use latent variable mod...

Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment

Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold u...

Subspace Models for Speech Transitions Using Principal Curves

Recognition of Expressions Based on Kernel Global and Local Symmetrical Weighted Fisher Discriminant Nonlinear Subspace Approach

Preservation of global and local features of images during dimensional reduction is a challenging task. The main goal of this work is to resolve the problem of singularity matrix by preserving local and global discriminative features by introducing symmetrical weights on principal components. To meet this goal Combinational Entire Gabor Kernel Global and Locality Preserving Symmetrical Weighted...