Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data
نویسندگان
چکیده
منابع مشابه
Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data
Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides whic...
متن کاملA New Framework for Distributed Multivariate Feature Selection
Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR featu...
متن کاملFeature selection using genetic algorithm for classification of schizophrenia using fMRI data
In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component analysis (ICA) is used for further data analysis. It estimates independent components (ICs) of...
متن کاملRandom subspace method for multivariate feature selection
In a growing number of domains the data collected has a large number of features. This poses a challenge to classical pattern recognition techniques, since the number of samples often is still limited with respect to the feature size. Classical pattern recognition methods suffer from the small sample size, and robust classification techniques are needed. In order to reduce the dimensionality of...
متن کاملTemporal Feature Selection for fMRI Analysis
Recent work in neuroimaging has shown that it is possible to classify cognitive states from functional magnetic resonance images (fMRI). Machine learning classifiers such as Gaussian Naive Bayes, Support Vector Machines, and Nearest Neighbors have all been applied successfully to this domain. Although it is a natural question to ask which classifiers work best, research has shown that the accur...
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: Computational and Mathematical Methods in Medicine
سال: 2013
ISSN: 1748-670X,1748-6718
DOI: 10.1155/2013/645921