Signal Detection in Singular Value Decomposition
Mohsen Rakhshan
TL;DR
This paper introduces an iterative SVD method for jointly analyzing multiple data matrices to detect correlated signals, with applications in supervised big data analysis involving complex phenotypes.
Contribution
The paper presents a novel iterative SVD algorithm that enables joint analysis of multiple matrices for signal detection, advancing multivariate data analysis techniques.
Findings
Effective detection of correlated signals across data matrices.
Application to big data with complex phenotypes demonstrated.
Enhanced analysis capability over traditional SVD methods.
Abstract
We develop an Iterative version of the Singular Value Decomposition (ISVD) that jointly analyzes a finite number of data matrices to identify signals that correlate among the rows of matrices. It will be illustrated how the supervised analysis of a big data set by another complex, multi-dimensional phenotype using the ISVD algorithm could lead to signal detection.
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Taxonomy
TopicsFractal and DNA sequence analysis · Gene expression and cancer classification · Blind Source Separation Techniques