Fundamentals of speech recognition
Fundamentals of speech recognition
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enhanced 1-NN time series classification using badness of records
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Classification of multivariate time series using two-dimensional singular value decomposition
Knowledge-Based Systems
Classification of multivariate time series using locality preserving projections
Knowledge-Based Systems
Learning a Kernel Matrix for Time Series Data from DTW Distances
Neural Information Processing
Discovering key sequences in time series data for pattern classification
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
The 1¢ Recognizer: a fast, accurate, and easy-to-implement handwritten gesture recognition technique
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
Order-Preserving sparse coding for sequence classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A new distance for probability measures based on the estimation of level sets
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both labeled data and unlabeled data are given beforehand, we consider three embeddings, embedding in a Euclidean space by MDS, embedding in a Pseudo-Euclidean space, and embedding in a Euclidean space by the Laplacian eigenmap technique. We have found through analysis and experiment that the embedding by the Laplacian eigenmap method leads to the best classification result. Furthermore, the proposed approach with Laplacian eigenmap embedding shows better performance than k-nearest neighbor method.