Abstract
Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data. This paper makes an important attempt to analyze a symbolic data set for dimensionality reduction when the features are of histogram type. The result of an in-depth analysis of such a histogram data set has lead to proposing basic arithmetic and definitions related to histogram data. The basic arithmetic has been used for dimensionality reduction modeling of histogram data set through Histogram PCA. The modeling procedure is demonstrated by experiments with 700x3 data, iris data and 80X data. The utility/applicability of Histogram PCA is validated by clustering the above data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Diday, E.: An Introduction to Symbolic Data Analysis and the Sodas Software. Journal of symbolic Data Analysis 1 (July 2002)
Diday, E.: The symbolic approach in clustering and related methods of data analysis. In: Bock, H.H. (ed.) Classification Methods of Data Analysis, North-Holland, Amsterdam (1988)
Gowda, K.C., Diday, E.: Symbolic Clustering Using a New Similarity Measure. IEEE Transaction on Systems, Machines and Cybernetics 22(2) (1992)
Gowda, K.C., Diday, E.: Symbolic Clustering Using a New Dissimilarity Measure. Journal of Pattern Recognition 24(6) (1991)
Nagabhushan, P.: An Efficient Method for Classifying Remotely Sensed Data, Incorporating Dimensionality Reduction. Ph.D. Thesis, University of Mysore, India (1998)
Nagabhushan, P., Gowda, K.C., Diday, E.: Dimensionality Reduction of Symbolic Data. Pattern Recognition Letters 16, 219–223 (1995)
Bock, H.H., Diday, E.: Analysis of Symbolic Data- Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (1998)
Kumar, R.P., Nagabhushan, P.: Multiresolution Knowledge Mining using Continuous Wavelet Transform. In: Proc. of International Conference on Cognition and Recognition (2005)
Jolliffe, I.T.: Principal component analysis, 2nd edn. Springer, New York (2002)
Manley, B.F.J.: Multivariate Statistical Methods: a Primer. Chapman and Hall, Bury St. Edmunds (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Nagabhushan, P., Pradeep Kumar, R. (2007). Histogram PCA. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_120
Download citation
DOI: https://doi.org/10.1007/978-3-540-72393-6_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
eBook Packages: Computer ScienceComputer Science (R0)