Comparison between Network Component Analysis and Independent Component Analysis
碩士 === 國立中正大學 === 統計科學所 === 95 === Network component analysis (NCA) and independent component analysis (ICA) both are the ways of redundancy reduction. These statistical methods are for transforming an observed multidimensional random vector into lowerdimension. In this thesis, we use two different...
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| Format: | Others |
| Language: | en_US |
| Published: |
2007
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| Online Access: | http://ndltd.ncl.edu.tw/handle/44160739111819490954 |
| Summary: | 碩士 === 國立中正大學 === 統計科學所 === 95 === Network component analysis (NCA) and independent component analysis (ICA) both are the ways of redundancy reduction. These statistical methods are for transforming an observed multidimensional random vector into lowerdimension. In this thesis, we use two different algorithms for linear ICA: fast fixed-point algorithm and joint approximate diagonalization of eigenmatrices algorithm. We compare these three techniques that the ability of
reconstructing the hidden regulatory layers, via the simulation studies and a real data examples. We also investigate the sensitivity of the reconstructed signals to inaccuracies in the strength of network connectivity.
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