Probabilistic Sequential Independent Components Analysis
نویسندگان
چکیده
منابع مشابه
Probabilistic Analysis of Kernel Principal Components
This paper presents a probabilistic analysis of kernel principal components by unifying the theory of probabilistic principal component analysis and kernel principal component analysis. It is shown that, while the kernel component enhances the nonlinear modeling power, the probabilistic structure offers (i) a mixture model for nonlinear data structure containing nonlinear sub-structures, and (i...
متن کاملDynamic Competitive Probabilistic Principal Components Analysis
We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be...
متن کاملIndependent Components Analysis through Product Density Estimation
We present a simple direct approach for solving the ICA problem, using density estimation and maximum likelihood. Given a candidate orthogonal frame, we model each of the coordinates using a semi-parametric density estimate based on cubic splines. Since our estimates have two continuous derivatives , we can easily run a second order search for the frame parameters. Our method performs very favo...
متن کاملNoisy independent component analysis of autocorrelated components.
We present a method for the separation of superimposed, independent, autocorrelated components from noisy multichannel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account, and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the high-noise regime. Characteristics of the measu...
متن کاملIndependent Components Analysis for Signal Separation andDimension
We introduce an entropy maximisation method for extracting K signals from M K mixtures of N M source signals. The mixtures x = (x1; : : : ; xM) T are formed by combining diierent proportions of the independent sources s = (s1;
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks
سال: 2004
ISSN: 1045-9227
DOI: 10.1109/tnn.2004.828765