Multi-view Sequence-data Representation and Non-metric Distance-function Learning

A constant false alarm rate (CFAR) apparatus is responsive to detected transitions in the clutter signal level to remove certain range cells from the alarm threshold calculation process, whereby the CFAR detector retains maximum sensitivity in the vicinity of clutter transitions while avoiding increased false alarm rates. A transition detection apparatus searches for transitions in range cells preceding the threshold calculation window and controls switches which remove range cells from the threshold calculation as the transition is shifted into and through the threshold calculation window.

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

[2]  Wesley W. Chu,et al.  Segment-based approach for subsequence searches in sequence databases , 2001, Comput. Syst. Sci. Eng..

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

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

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

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

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

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

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

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

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

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

[13]  G. Chartrand Introductory Graph Theory , 1984 .

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

[15]  Robert P. W. Duin,et al.  A Generalized Kernel Approach to Dissimilarity-based Classification , 2002, J. Mach. Learn. Res..

[16]  Zheng Zhang,et al.  An Analysis of Transformation on Non - Positive Semidefinite Similarity Matrix for Kernel Machines , 2005, ICML 2005.

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

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

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

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

[21]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[22]  Christos Faloutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[23]  Klaus Obermayer,et al.  Classi cation on Pairwise Proximity , 2007 .

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

[25]  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.

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

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

[28]  Joachim M. Buhmann,et al.  Optimal Cluster Preserving Embedding of Nonmetric Proximity Data , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[31]  Eamonn J. Keogh,et al.  A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering , 2005, PAKDD.

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

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

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

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

[36]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

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

[38]  Edward Y. Chang,et al.  Distance-function design and fusion for sequence data , 2004, CIKM '04.

[39]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

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

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

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

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

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