The Uncertainty and Robustness of the Principal Component Analysis as a Tool for the Dimensionality Reduction

Abstract:

Experimental studies very often lead to datasets with a large number of noted attributes (observed properties) and relatively small number of records (observed objects). The classic analysis cannot explain recorded attributes in the form of regression relationships due to lack of sufficient number of data points. One of method making available a filtering of unimportant attributes is an approach known as ‘dimensionality reduction’. Well-known example of such approach is principal component analysis (PCA) which transforms the data from the high-dimensional space to a space of fewer dimensions and gives heuristics to select least but necessary number of dimensions. Authors used such technique successfully in their previous investigations but a question arose: whether PCA is robust and stable This paper tries to answer this question by re-sampling experimental data and observing empirical confidence intervals of parameters used to make decision in PCA heuristics.

• Periodical: Solid State Phenomena (Volume 235)
• Pages: 1-8
• DOI: https://doi.org/10.4028/www.scientific.net/SSP.235.1
• Online since: July 2015
• Authors: Jacek Pietraszek, Ewa Skrzypczak-Pietraszek*
• Keywords: Bootstrap, Cluster Analysis, Jackknife, Lamiaceae, Melittis Melissophyllum, Principal Component Analysis, Reduction of Dimensionality, Robustness, Uncertainty

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