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Semi supervised dimensionality reduction and its application in astronomical spectral data in

Author: ShengYing
Tutor: ZhangJiFu; YangHaiFeng
School: Taiyuan University of Science and Technology
Course: Computer Software and Theory
Keywords: Semi-supervised learning PCA Spectral data Fisher discriminantanalysis Characteristic dimension reduction
CLC: TP311.13
Type: Master's thesis
Year: 2012
Downloads: 43
Quote: 0
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Abstract


Data dimension reduction can find a low dimension expression of highdimensional data based on specific laws in a given sample space，and it cankeep inner information of the original data. Data dimension reduction mainlysolve disaster problem. Data dimension reduction is divided into three differenttypes according to category information,it is supervised data dimensionreduction,unsupervised data dimension reduction and semi-supervised datadimension reduction.The semi-supervised data dimension reduction is builtbased on traditional algorithm of FDA and PCA.The paper realizesemi-supervision dimension reduction in the spectral data,with semi-supervisiondimension reduction as the foundation. At the same time, the paper study choiceof semi-supervised dimension reduction, choice of label information.Its mainresearch results are introduced in the next:(1) The semi-supervised framework is built about FDA and PCA. Thefisher discriminant analysis and PCA are compared in the framework, andshortage in data feature extraction is pointed out. The fisher discriminantanalysis overfit the label data in the dimension reduction.The PCA can noteffectively use label information.The experiment check the result of analysis.(2)The semi-supervised dimension reduction of spectral characteristicbased on fisher discriminant analysis is presented. Firstly, for celestial spectraldata, an uncertainty relation is established in which fisher discriminant analysisand PCA can be selected variably. Secondly, the global optimization ofsemi-supervised dimensionality reduction is built. Dimensionality reductionresults are calculated through the eigenvalue decomposition,So that the problemof over-fitting is solved in astronomical spectral data dimensionality reduction.In the end, The method is validated validity in the experiment by using thehzqso and mstar astronomical spectral features line data sets.

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