Sequence Alignment by Regression Coding

In aligning two sequences, dynamic time warping (DTW) is the well-known dynamic programming algorithm. However, DTW can be sensitive to noise samples that may affect alignment of other relevant samples. In this article, we propose a novel approach to sequence alignment by treating it as a regression coding optimization problem, a task to predict one sequence from another. With some mild relaxation DTW can be seen as a special case of our approach while we provide more flexible and informative interpretation. Experimental results on both synthetic and real-world datasets show that our method can yield more accurate alignment than existing approaches.

[1]  Rui Li,et al.  Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[2]  Eamonn J. Keogh,et al.  UCR Time Series Data Mining Archive , 1983 .

[3]  Syed Abdul Rahman Al-Haddad,et al.  Robust Speech Recognition Using Fusion Techniques and Adaptive Filtering , 2009 .

[4]  George M. Church,et al.  Aligning gene expression time series with time warping algorithms , 2001, Bioinform..

[5]  Joachim M. Buhmann,et al.  Time-series alignment by non-negative multiple generalized canonical correlation analysis , 2007, BMC Bioinformatics.

[6]  Ying Xie,et al.  Adaptive Feature Based Dynamic Time Warping , 2010 .

[7]  Nuria Oliver,et al.  Partial sequence matching using an Unbounded Dynamic Time Warping algorithm , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Mohammed Waleed Kadous,et al.  Learning Comprehensible Descriptions of Multivariate Time Series , 1999, ICML.

[9]  Vit Niennattrakul,et al.  On Clustering Multimedia Time Series Data Using K-Means and Dynamic Time Warping , 2007, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07).

[10]  Rawesak Tanawongsuwan,et al.  Characteristics of Time-Distance Gait Parameters Across Speeds , 2003 .

[11]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[12]  Xiaoli Zhou,et al.  Face recognition from face profile using dynamic time warping , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  F. Itakura,et al.  Minimum prediction residual principle applied to speech recognition , 1975 .

[14]  Sean R. Eddy,et al.  Profile hidden Markov models , 1998, Bioinform..

[15]  Eamonn J. Keogh,et al.  Derivative Dynamic Time Warping , 2001, SDM.

[16]  David Haussler,et al.  Using the Fisher Kernel Method to Detect Remote Protein Homologies , 1999, ISMB.

[17]  Eamonn J. Keogh,et al.  Three Myths about Dynamic Time Warping Data Mining , 2005, SDM.

[18]  Björn W. Schuller,et al.  A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams , 2009, Neurocomputing.

[19]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[20]  Dipankar Dasgupta,et al.  Novelty detection in time series data using ideas from immunology , 1996 .

[21]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .