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
[1] Fu T. A review on time series data mining[J]. Engineering Applications of Artificial Intelligence,2011,24(1):164-181.
[2] Batista G E,Wang X,Keogh E J. A complexity-invariant distance measure for time series[C]∥Proceedings of the 2011 SIAM International Conference on Data Mining,2011,11:699-710.
[3] Esling P,Agon C,Time-series data mining[J]. ACM Computing Surveys (CSUR),2012,45(1):1-12.
[4] Wang X,Mueen A,Ding H,et al. Experimental comparison of representation methods and distance measures for time series data[J]. Data Mining and Knowledge Discovery,2013,26(2):275-309.
[5] 吴红花,刘国华,王伟. 不确定时间序列的相似性匹配问题[J]. 计算机研究与发展,2014,51(8):1802-1810.
Wu Hong-hua,Liu Guo-hua,Wang Wei. Similarity matching for uncertain time series[J]. Journal of Computer Research and Development,2014,51(8):1802-1810.
[6] Scott S,Varian H. Predicting the present with bayesian structural time series[J]. International Journal of Mathematical Modelling and Numerical Optimisation,2014,5(1):4-23.
[7] Keogh E,Chakrabarti K,Pazzani M,et al. Dimensionality reduction for fast similarity search in large time series databases[J]. Knowledge and Information Systems,2001,3(3):263-286.
[8] Chakrabarti K,Keogh E,Mehrotra S,et al. Locally adaptive dimensionality reduction for indexing large time series databases[J]. ACM Transactions on Database Systems,2002,27(2):188-228.
[9] Xing E P,Jordan M I,Russell S,et al. Distance metric learning with application to clustering with side-information[J].Advances in Neural Information Processing Systems,2003:15:505-512.
[10] 邹朋成,王建东,杨国庆,等. 辅助信息自动生成的时间序列距离度量学习[J]. 软件学报,2013,24(11):2642-2655.
Zou Peng-cheng,Wang Jian-dong,Yang Guo-qing,et al. Distance metric learning based on side information autogeneration for time series[J]. Journal of Software,2013,24(11):2642-2655.
[11] Jolliffe I. Principal Component Analysis[M].USA: John Wiley & Sons,2005.
[12] Abdi H,Williams L J. Principal component analysis[J]. Wiley interdisciplinary reviews:computational statistics,2010,2(4):433-459.
[13] Lin J,Keogh E,Lonardi S,et al. A symbolic representation of time series, with implications for streaming algorithms[C]∥ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,San Diego,USA,2003:2-11.
[14] Lin J,Keogh E,Wei L,et al. Experiencing SAX:a novel symbolic representation of time series[J]. Data Mining and Knowledge Discovery,2007,15(2):107-144.
[15] Ahmed A,Bakar A,Hamdan A. Harmony search algorithm for optimal word size in symbolic time series representation[C]∥IEEE 2011 3rd Conference on Data Mining and Optimization,Bangi,Malaysia,2011:57-62.
[16] Park S,Kim S W,Chu W W. Segment-based approach for subsequence searches in sequence databases[C]∥Proceedings of the 2001 ACM Symposium on Applied Computing,New York,USA,2001:248-252.
[17] 王寅同,王建东,陈海燕,等. 基于概率类和不相关判别的半监督局部Fisher方法[J]. 控制与决策,2015,30(1):32-38.
Wang Yin-tong,Wang Jian-dong,Chen Hai-yan,et al. Semi-supervised local Fisher method based on probability class and uncorrelated discriminant[J]. Control and Decision,2015,30(1):32-38.
[18] Fisher R. The Use of multiple measurements in taxonomic problems[J]. Annals of Eugenics,1936,7(2):179-188.
[19] Sakoe H,Chiba S. Dynamic programming algorithm optimization for spoken word recognition[C]∥IEEE Transactioin on Acoustics,Speech and Signal Processing,1978,26(1):43-49.
[20] Xi X,Keogh E,Shelton C,et al. Fast time series classification using numerosity reduction[C]∥In Process of the International Conference on Machine Learning,New York,USA,2006:1033-1040.
[21] Rakthanmanon T,Campana B J,Mueen A,et al. Searching and mining trillions of time series subsequences under dynamic time warping[C]∥In Process of the International Conference on Knowledge Discovery and Data Mining,New York,USA,2012:262-270.
[22] Keogh E. Exact indexing of dynamic time warping[J].Knowledge and Information Systems,2005:7(3):358-386.
[23] Shimodaira H,Noma K,Nakai M,et al. Dynamic time-alignment kernel in support vector machine[J].Advances in Neural Information Processing Systems,2002,14:921-928.
[24] Cuturi M,Vert J P,Birkenes O,et al. A kernel for time series based on global alignments[C]∥IEEE Computer Society on Acoustics,Speech and Signal Processing,Honolulu,HI,2007:413-416.
[25] Cuturi M. Fast global alignment kernels[C]∥In Process of the International Conference on Machine Learning,ACM,Bellevue,USA,2011:929-936.
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