Constructing a linear discrete system in Kernel space as a supervised classifier
C Florintina, E. S. Gopi · 2017
The pattern recognition techniques involve feature extraction from the data, dimensionality reduction (like PCA, LDA, K-LDA, etc) and constructing a classifier (NN, NM, SVM, etc.) using the training set and validating the constructed classifier using the testing set. The usage of digital signal processing (DSP) techniques in pattern recognition is always limited to the feature extraction stage such as collecting the Fourier, wavelet co-efficients, HMM, GMM, etc. In this paper we explore the usage of classical DSP techniques like convolution, FIR filter to construct the classifier and compare it with the state of the art techniques. The proposed technique paves the alternative way to construct a classifier that is helpful for Big data analysis.