Selecting inputs for modeling using normalized higher order statistics and independent component analysis
نویسندگان
چکیده
منابع مشابه
Selecting inputs for modeling using normalized higher order statistics and independent component analysis
The problem of input variable selection is well known in the task of modeling real-world data. In this paper, we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches w...
متن کاملON AN INDEPENDENT RESULT USING ORDER STATISTICS AND THEIR CONCOMITANT
Let X1;X2;...;Xn have a jointly multivariate exchangeable normal distribution. In this work we investigate another proof of the independence of X and S2 using order statistics. We also assume that (Xi ; Yi); i =1; 2;...; n; jointly distributed in bivariate normal and establish the independence of the mean and the variance of concomitants of order statistics.
متن کاملInput Variable Selection Using Independent Component Analysis and Higher Order Statistics
In real world problems of nonlinear model building there may be a number of inputs available for use. However, a common problem is that we do not know which inputs are necessary for the model. Previous methods have difficulties in coping with dependent inputs. In this paper, we propose a novel method of input variable selection based on independent component analysis and higher order cross stat...
متن کاملEnhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.
Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, h...
متن کاملAlgorithms for Independent Components Analysis and Higher Order Statistics
A latent variable generative model with finite noise is used to describe several different algorithms for Independent Components Analysis (ICA). In particular, the Fixed Point ICA algorithm is shown to be equivalent to the ExpectationMaximization algorithm for maximum likelihood under certain constraints, allowing the conditions for global convergence to be elucidated. The algorithms can also b...
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: 2001
ISSN: 1045-9227
DOI: 10.1109/72.925564