Global Journal of Information Technology Volume 06, Issue 1, (2016) 11-17 http://sproc.org/ojs/index.php/gjit Kernel principal component analysis for multimedia retrieval Guang-Ho Cha *, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. Suggested Citation: Cha, G.H. (2016). Kernel principal component analysis for multimedia retrieval. Global Journal of Information Technology. 6(1), 11-17. Received 25 January, 2016; revised 18 February, 2016; accepted 10 March, 2016. Selection and peer review under responsibility of Prof. Dr. Adem Karahoca, Bahcesehir University, Turkey © 2016 SciencePark Research, Organization & Counseling. All rights reserved. Abstract Principal component analysis (PCA) is an important tool in many areas including data reduction and interpretation, information retrieval, image processing, and so on. Kernel PCA has recently been proposed as a nonlinear extension of the popular PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This paper illustrates the potential of kernel PCA for dimensionality reduction and feature extraction in multimedia retrieval. By the use of Gaussian kernels, the principal components were computed in the feature space of an image data set and they are used as new dimensions to approximate image features. Extensive experimental results show that kernel PCA performs better than linear PCA with respect to the retrieval quality as well as the retrieval precision in content-based image retrievals. Keywords: Principal component analysis, kernel principal component analysis, multimedia retrieval, dimensionality reduction, image retrieval *ADDRESS FOR CORRESPONDENCE: Guang-Ho Cha, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. E-mail address: ghcha@snut.ac.kr