Affiliation:
1. Department of Biostatistics Yale University New Haven Connecticut USA
2. Department of Statistics and Probability Michigan State University East Lansing Michigan USA
Abstract
AbstractA fundamental problem in functional data analysis is to classify a functional observation based on training data. The application of functional data classification has gained immense popularity and utility across a wide array of disciplines, encompassing biology, engineering, environmental science, medical science, neurology, social science, and beyond. The phenomenal growth of the application of functional data classification indicates the urgent need for a systematic approach to develop efficient classification methods and scalable algorithmic implementations. Therefore, we here conduct a comprehensive review of classification methods for functional data. The review aims to bridge the gap between the functional data analysis community and the machine learning community, and to intrigue new principles for functional data classification.This article is categorized under:
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
Statistical Models > Classification Models
Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Funder
National Science Foundation
Donald R. and Esther Simon Foundation
Subject
Statistics and Probability