THE APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO THE IDENTIFICATION OF FAGACEAE LEAF FOSSILS
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Abstract:
Abundant fossil records show that the Fagaceae has remained a dominant component in the Northern Hemisphere since the Cenozoic.However,due to the large number of living species,it is not easy to identify leaves to a particular species.Consequently,the identification of fossil leaves belonging to the Fagaceae is problematic. In the face of incomplete characteristics information about fossil specimens,it is necessary to eliminate redundant features and to focus on the features which can determine the identification of fossil specimens.Based on an investigation of various dimensionality reduction algorithms,in which the numerical characteristics of encoding and assign-ment of plants were considered,it is shown that principal component analysis is the best method to choose primary identifying characteristics.In this study,taking Quercus subgenus Cyclobalanopsis(Fagaceae)as an example,we use principal component analysis to eliminate subordinate features from the 22 traits which may be observed in the fossil leaves.The result demonstrates that 10 characteristics play a major role in the classification and identification of Fagaceae fossil leaves.The outcome is applied to the identification of Quercus subgenus Cyclobalanopsis fossil specimens collected from the Late Miocene of Tiantai,Zhejiang,eastern China.The results show that even if only the 10 main characters picked out by principal component analysis compression are utilized,these can achieve an accurate identification of fossil Fagaceae.Adding the redundant features does not improve the taxonomic resolution.The principal component analysis method is verified as the most effective method to eliminate the subordinate Fagaceae leaf traits.
