吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1202-1208.doi: 10.13229/j.cnki.jdxbgxb201604028

• Orginal Article • Previous Articles     Next Articles

Time series dimensionality reduction and aircraft model recognition in airport-noise

WANG Yin-tong, WANG Jian-dong, CHEN Hai-yan   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2015-01-27 Online:2016-07-20 Published:2016-07-20

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Abstract: In order to enhance the efficiency and effectiveness of the processing of high dimensional airport-noise data with incomplete labels, the time series dimensionality reduction and aircraft model recognition in airport-noise are investigated. First, we use semi-supervised local fisher method, which is based on probability class and uncorrelated discriminant (SLFisher), to obtain the transformation matrix of dimensionality reduction. Next, the high dimensional time series data are mapped to the low-dimensional space. Finally, we apply the k Nearest Neighbor (kNN) classifier to classify the obtained low-dimensional data. Experimental results on measured airport-noise data demonstrate that the performance of aircraft model recognition is remarkably improved after the dimensionality reduction achieved using SLFisher.

Key words: artificial intelligence, airport-noise, time series, dimensionality reduction, aircraft models recognition

CLC Number: 

  • TP18
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