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Functional classwise principal component analysis: a classification framework for functional data analysis
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DOI：10.1007/s10618-022-00898-1.png)
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Abstract
En 中文
In recent times, functional data analysis has been successfully applied in the field of high dimensional data classification. In this paper, we present a classification framework using functional data and classwise Principal Component Analysis (PCA). Our proposed method can be used in high dimensional time series data which typically suffers from small sample size problem. Our method extracts a piecewise linear functional feature space and is particularly suitable for hard classification problems. The proposed framework converts time series data into functional data and uses classwise functional PCA for feature extraction followed by classification using a Bayesian linear classifier. We demonstrate the efficacy of our proposed method by applying it to both synthetic data sets and real time series data from diverse fields including but not limited to neuroscience, food science, medical sciences and chemometrics.
Keywords:
Classification
Functional data analysis
Functional principal component analysis
Classwise principal component analysis
Gram–Schmidt orthogonalization
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