By Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau
This e-book constitutes the refereed court cases of the 1st ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016.
The eleven complete papers awarded have been rigorously reviewed and chosen from 22 submissions. the 1st half specializes in studying new representations and embeddings for time sequence type, clustering or for dimensionality aid. the second one half offers ways on category and clustering with not easy functions on medication or earth remark info. those works convey other ways to think about temporal dependency in clustering or category methods. The final a part of the publication is devoted to metric studying and time sequence comparability, it addresses the matter of speeding-up the dynamic time warping or facing multi-modal and multi-scale metric studying for time sequence class and clustering.
Read or Download Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers PDF
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Additional info for Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers
For all experiments with dense extraction, we set τstep = 1, and we extract keypoints at all scales. Using such a value for τstep enables one to have a suﬃcient number of keypoints even for small time series, and guarantees that keypoint neighborhoods overlap so that all subparts of the time series are described. 2 Dense Extraction vs. Scale-Space Extrema Detection Figure 2 shows a pairwise comparison of error rates between BoTSW and its dense counterpart D-BoTSW for all datasets in the UCR repository.
J. Mach. Learn. Res. 10, 747–776 (2009) 6. : Support-vector network. Mach. Learn. 20, 273–297 (1995) 7. : Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002) 8. : Experiments with object based discriminant functions; a featureless approach to pattern recognition. Pattern Recogn. Lett. 18(11–13), 1159–1166 (1997) 9. : The dissimilarity space: bridging structural and statistical pattern recognition. Pattern Recogn. Lett. 33(7), 807–962 (2012) 10. : A review on time series data mining.
In [15,21] the full pairwise DTW distance matrix is used for support vector learning. This contribution extends prior work  on exploring the eﬀects of dimensionality reduction applied to the pairwise distance matrix. The proposed approach applies principal component analysis (PCA) for dimension reduction of the dissimilarity representations followed by training a linear and non-linear support vector machine (SVM). Experimental results on 42 datasets show that SVM classiﬁers performed better on average than the nearest-neighbor classiﬁer using DTW distance.
Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers by Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau