  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.
