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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Convolution-based linear discriminant analysis for functional data classification

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Author(s):
Guzman, Grover E. Castro [1] ; Fujita, Andre [1]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo Rua Matao, Inst Math & Stat, Dept Comp Sci, BR-05508090 Sao Paulo, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: INFORMATION SCIENCES; v. 581, p. 469-478, DEC 2021.
Web of Science Citations: 0
Abstract

Technological advances have allowed for the rise red in more reliable and less expensive sensors to collect data over time (e.g., on temperature, heartbeat, and neural activity). Consequently, mathematical methods to examine these time series data have become necessary. One topic of intensive research in time series analysis is supervised classification. For example, biomedical researchers are interested in classifying controls versus people with heart disorders based on electrocardiograms. Several works have adapted Fisher's linear discriminant analysis (LDA) to work with functional data. However, they have poor performance when a multiplicative random effect model generates the time series; they do not exploit the periodicity of the data. To solve this problem, we propose convolution based linear discriminant analysis (cLDA). Different from the standard LDA that projects the data into a lower space, cLDA obtains filters. To show the performance of cLDA, we compared it to state-of-the-art methods in simulated and 12 empirical datasets. cLDA obtained the lowest classification error rate on average. It showed the ability to classify real-world time series. (c) 2021 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 18/17996-5 - Stratification of psychiatric disorders by using network discriminant and clustering analyses
Grantee:André Fujita
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
Grantee:André Fujita
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 20/01479-1 - Classification of body/mental states for a human-machine interface based on the heart rate variability
Grantee:André Fujita
Support Opportunities: Research Grants - eScience and Data Science Program - Regular Program Grants