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Feature extraction and classification using leading eigenvectors: Applications to biomedical and multi-modal mHealth data
journal contribution
posted on 2019-08-16, 10:31 authored by Georgina CosmaGeorgina Cosma, T Martin McginnityEigendecomposition is the factorization of a matrix into its canonical form, whereby the matrix
is represented in terms of its eigenvalues and eigenvectors. A common step is the reduction of the data to a
kernel matrix also known as a Gram matrix which is used for machine learning tasks. A significant drawback
of kernel methods is the computational complexity associated with manipulating kernel matrices. This paper
demonstrates that leading eigenvectors derived from singular value decomposition (SVD) and Nyström
approximation methods can be utilized for classification tasks without the need to construct Gram matrices.
Experiments were conducted with 14 biomedical datasets to compare classifier performance when taking
as input into a classifier matrices containing: 1) leading eigenvectors which result from each approximation
method, and 2) matrices which result from constructing the patient-by-patient Gram matrix. The results
provide evidence to support the main hypothesis of this paper that using the leading eigenvectors as input into
a classifier significantly (p < 0.05) improves classifier performance in terms of accuracy and time compared
to using Gram matrices. Furthermore, experiments were carried out using large multi-modal mHealth time
series datasets of ten different subjects with diverse profiles while they were performing several physical
activities. Experiments with the mHealth datasets utilized a sequential deep learning model. The significance
of the proposed approach is that it can make feature extraction methods more accessible on large-scale
unimodal and multi-modal data which are becoming common in many applications.
Funding
The Leverhulme Trust Research through the Novel Approaches for Constructing Optimized Multimodal Data Spaces Project under Grant RPG-2016-252
History
School
- Science
Department
- Computer Science
Published in
IEEE AccessVolume
7Pages
107400 - 107412Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- VoR (Version of Record)
Publisher statement
This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/Acceptance date
2019-07-10Publication date
2019-08-02Copyright date
2019eISSN
2169-3536Publisher version
Language
- en