Internal Structure Identification of Random Process Using Principal Component Analysis
Principal component analysis (PCA) is known to be a powerful linear technique for data set dimensionality reduction. This paper focuses on revealing the essence of PCA to interpret the data, which is to identify the internal structure of the random process from a large experimental data set. We give an explanation of the PCA procedure performed on a generated data set to demonstrate the exact meaning of the dimensionality reduction. Especially, a method is proposed to precisely determine the...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the International Conference on Signal Processing and Communication Systems (ICSPCS 2010)|
|01_Zhang_Internal_Structure_2010.pdf||155.19 kB||Adobe PDF||Request a copy|
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