Functional Classwise Principal Component Analysis: A Novel Classification Framework
This work addresses classification challenges in high-dimensional time series data for domains such as neuroscience and food science, but it appears incremental as it builds on existing functional data analysis and PCA methods.
The paper tackles the problem of classifying high-dimensional time series data with small sample sizes by introducing a novel classification framework that converts time series into functional data and uses classwise functional PCA for feature extraction, followed by a Bayesian linear classifier, demonstrating efficacy on synthetic and real datasets from fields like neuroscience and chemometrics.
In recent times, functional data analysis (FDA) has been successfully applied in the field of high dimensional data classification. In this paper, we present a novel 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 piece wise 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.