Paper Info
Title
Adaptive Subspace Detector In High Dimensional Space With Insufficient Training Data
Abstract
Adaptive subspace detectors (ASD) generalize matched subspace detectors (MSD) by accounting for possible correlation. Both ASD and MSD are derived using the generalized likelihood ratio test (GLRT). While MSD assumes there is no correlation between observations, ASD estimates a sample covariance matrix of possibly correlated samples using signalfree observations. In this paper, we address the performance of the ASD when the number of secondary data is insufficient and the observed signal lies in higher dimensional space. Such high dimensional spaces are frequently encountered in functional magnetic resonance imaging (fMRI) data for the analysis of brain activation detection. We propose a methodology that works based on the latent variables in a lower dimensional space. A low-rank decomposition of the sample covariance matrix is derived based on the singular value de-composition (SVD) and an adaptive basis selection method is used to decide which eigen-vectors are useful in data projection. Performing detection in the lower dimensional subspace has the benefit of reducing the number of parameters which need to be estimated. Simulation results show superiority of our proposed adaptive reduced subspace detector (ARSD) over conventional ASD in term of probability of detection.
Year
DOI
Venue
2019
10.1109/icassp.2019.8683677
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Detection, Likelihood Ratio Test, fMRI and Adaptive Reduced Subspace Detector
Singular value decomposition,Likelihood-ratio test,Pattern recognition,Subspace topology,Computer science,Latent variable,Correlation,Artificial intelligence,High dimensional space,Detector,Statistical power
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Aref Miri Rekavandi101.01
Abd-Krim Seghouane2195.55
R. J. Evans3184.48