Distance-function design and fusion for sequence data

Sequence-data mining plays a key role in many scientific studies and real-world applications such as bioinformatics, data stream, and sensor networks, where sequence data are processed and their semantics interpreted. In this paper we address two relevant issues: sequence-data representation, and representation-to-semantics mapping. For representation, since the best one is dependent upon the application being used and even the type of query, we propose representing sequence data in multiple views. For each representation, we propose methods to construct a <i>valid kernel</i> as the distance function to measure <i>similarity</i> between sequences. For mapping, we then find the best combination of the individual distance functions, which measure similarity of different views, to depict the target semantics. We propose a <i>super-kernel function-fusion</i> scheme to achieve the optimal mapping. Through theoretical analysis and empirical studies on UCI and real world datasets, we show our approach of multi-view representation and fusion to be mathematically valid and very effective for practical purposes.

[1]  Nasser Yazdani,et al.  Matching and indexing sequences of different lengths , 1997, CIKM '97.

[2]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[3]  Joachim M. Buhmann,et al.  Going Metric: Denoising Pairwise Data , 2002, NIPS.

[4]  Lei Chen,et al.  Symbolic representation and retrieval of moving object trajectories , 2004, MIR '04.

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[7]  Bernhard Schölkopf,et al.  Dynamic Alignment Kernels , 2000 .

[8]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[9]  B. S. Manjunath,et al.  An Eigenspace Update Algorithm for Image Analysis , 1997, CVGIP Graph. Model. Image Process..

[10]  Park,et al.  Developing NLP Tools for Genome Informatics: An Information Extraction Perspective. , 1998, Genome informatics. Workshop on Genome Informatics.

[11]  Christos Faloutsos,et al.  Efficiently supporting ad hoc queries in large datasets of time sequences , 1997, SIGMOD '97.

[12]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[13]  C. Watkins.  Dynamic Alignment Kernels , 1999 .

[14]  Piotr Indyk,et al.  Identifying Representative Trends in Massive Time Series Data Sets Using Sketches , 2000, VLDB.

[15]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[16]  Philip S. Yu,et al.  Adaptive query processing for time-series data , 1999, KDD '99.

[17]  Edward Y. Chang,et al.  Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance , 2003, MULTIMEDIA '03.

[18]  Pierre Geurts,et al.  Pattern Extraction for Time Series Classification , 2001, PKDD.

[19]  John D. Lafferty,et al.  Diffusion Kernels on Graphs and Other Discrete Input Spaces , 2002, ICML.

[20]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[21]  Malcolm P. Atkinson,et al.  A Database Index to Large Biological Sequences , 2001, VLDB.

[22]  Nello Cristianini,et al.  Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.

[23]  C. Finney,et al.  A review of symbolic analysis of experimental data , 2003 .

[24]  Mike A. Steel,et al.  Metrics on RNA Secondary Structures , 2000, J. Comput. Biol..

[25]  Jignesh M. Patel,et al.  Searching on the Secondary Structure of Protein Sequences , 2002, VLDB.

[26]  Hannu Toivonen,et al.  Mining for similarities in aligned time series using wavelets , 1999, Defense, Security, and Sensing.

[27]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[28]  E. Kay,et al.  Introductory Graph Theory , 1978 .

[29]  Yoshua Bengio,et al.  Gradient-Based Optimization of Hyperparameters , 2000, Neural Computation.

[30]  Christus,et al.  A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins , 2022 .

[31]  Henrik André-Jönsson,et al.  Using Signature Files for Querying Time-Series Data , 1997, PKDD.

[32]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[33]  Volker Roth,et al.  Nonlinear Discriminant Analysis Using Kernel Functions , 1999, NIPS.