Hardware Efficient NIPALS Architecture for Principal Component Analysis of Hyper Spectral Images

Abstract

Principal Component Analysis (PCA) has been a major tool in performing characterization of environmental data where in, the data is typically a hyper spectral image. Using statistical methods, PCA is often capable of reducing the dimensionality of data. On the other hand Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm provides an efficient alternative for extracting the principal components with a minimum penalty on processing speed. In this work we provide the hardware implementation of NIPALS algorithm on an FPGA, for extracting principal components of a given dataset. Experimental results of our approach on various hyper spectral images show 92.31% average reduction in dimensionality with 0.1% average loss on information of the dataset. The results obtained from XILINX Artix-7 FPGA implementation show the advantage of the proposed method. More particularly, the proposed architecture gives an improvement in speed by factor of 15.71x compared to the state of art approaches.