Author

Kuldip K. Paliwal

Signal Processing Lab, School of Engineering, Faculty of Engineering and Information Technology, Griffith University, Australia

4 articles in our journals

Articles where this person is an author, newest first. People are matched by their name as printed (no ORCID iD is recorded for them), not by name alone; search the name as text to find namesakes too.

Research ArticleOpen accessA Gene Selection Algorithm using Bayesian Classification ApproachAlok Sharma, Kuldip K. PaliwalIn this study, we propose a new feature (or gene) selection algorithm using Bayes classification approach. The algorithm can find gene subset crucial for cancer classification problem. Problem statement: Gene identification plays important role in human cancer classification problem. Several feature selection algorithms have been proposed for analyzing and understanding influential genes using gene expression profiles. Approach: The feature selection … …American Journal of Applied Sciences, Vol. 9, Issue 1 · 24 Nov 2011 · 8,312 accessesView PDF Cite

Research ArticleOpen accessRotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality ReductionAlok Sharma, Kuldip K. PaliwalLinear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotates the classes individually … …Journal of Computer Science, Vol. 2, Issue 9 · 30 Sep 2006 · 6,589 accessesView PDF Cite

Research ArticleOpen accessSplitting Technique Initialization in Local PCAAlok Sharma, Kuldip K. Paliwal, Godfrey C. OnwuboluThe local Principal Component Analysis (PCA) reduces linearly redundant components that may present in higher dimensional space. It deploys an initial guess technique which can be utilized when the distribution of a given multivariate data is known to the user. The problem in initialization arises when the distribution is not known. This study explores a technique that can be easily … …Journal of Computer Science, Vol. 2, Issue 1 · 31 Jan 2006 · 7,725 accessesView PDF Cite

Research ArticleOpen accessPattern Classification: An Improvement Using Combination of VQ and PCA Based TechniquesAlok Sharma, Kuldip K. Paliwal, Godfrey C. OnwuboluThis study firstly presents a survey on basic classifiers namely Minimum Distance Classifier (MDC), Vector Quantization (VQ), Principal Component Analysis (PCA), Nearest Neighbor (NN) and K-Nearest Neighbor (KNN). Then Vector Quantized Principal Component Analysis (VQPCA) which is generally used for representation purposes is considered for performing classification tasks. Some classifiers achieve high classification accuracy but their data storage requirement and … …American Journal of Applied Sciences, Vol. 2, Issue 10 · 31 Oct 2005 · 6,504 accessesView PDF Cite