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《Spectroscopy and Spectral Analysis》 2014-05
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A Late-Type Star Spectra Outlier Data Mining System

CAI Jiang-hui;YANG Hai-feng;ZHAO Xu-jun;ZHANG Ji-fu;School of Computer Science and Technology,Taiyuan University of Science and Technology;  
In M star population,some special objects,which may be of magnetic activity,may be giant stars,or may be of other rare properties,are very important for the follow-up observation and the scientific research on galactic structure and evolution. For local bias of M-type star spectral characteristic lines contained in subspace,a late-type star spectra outlier data mining system is given in the present paper.Firstly,for the sample of M stellar spectral characteristic lines indices,its distribution characteristics in attribute spaces are measured by using the sparse factor and sparsity coefficient,and then this sample is discretized and dimension-reduced to the spectral subspace.Secondly,local outlier subspaces are extracted by PSO(particle swarm optimization) algorithm and identified.Additionally,the effects of sparse coefficient and sparse factor on the number of outliers are discussed by experiments on the sample of SDSS M stellar spectral line index set,and the outliers are compared with spectral type provided by SDSS.In this way,the feasibility and value of this system were validated.
【Fund】: 国家自然科学基金项目(41372349 61272263);; 山西省自然科学基金项目(2012011011-4)资助
【CateGory Index】: P144.1;TP311.13
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【Co-citations】
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