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.
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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
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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
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