Online Program Home
My Program

Abstract Details

Activity Number: 361
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318929
Title: Decoding Brain States from fMRI Data with a Machine Learning Method
Author(s): Elizabeth Chou*
Keywords: Two-way Clustering ; Two-layered Distance ; Supervised learning ; Classification ; Data Cloud Geometry ; fMRI

In order to extract the special patterns of brain activities from fMRI data, I propose a two-way clustering learning rule with a two-layered distance procedure to capture the data geometry in this research. The computations for clustering are primarily based on the new non-supervised learning algorithm, Data Cloud Geometry (DCG). By coupling the two DCG trees, one for the subject space and the other for the covariate space, many partial interaction patterns between subject-clusters and covariate-clusters can be determined. Also, I demonstrate that how to adaptively evolve a simple empirical distance into an effective one to facilitate an efficient global feature-matrix for learning purposes. Several datasets are demonstrated to show the efficiencies of my proposed methods. The methods can also be used in feature extraction. The advantages of my learning rule by comparing its performance to the other commonly used machine learning techniques will be demonstrated. This research provides an innovative method for fMRI data classification and brain states decoding.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association