By Ahlame Douzal-Chouakria, José A. Vilar, Pierre-François Marteau
This booklet constitutes the refereed complaints of the 1st ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016.
The eleven complete papers provided have been rigorously reviewed and chosen from 22 submissions. the 1st half makes a speciality of studying new representations and embeddings for time sequence category, clustering or for dimensionality relief. the second one half provides methods on type and clustering with difficult functions on medication or earth remark information. those works express other ways to think about temporal dependency in clustering or type strategies. 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 category 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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This booklet constitutes the refereed lawsuits of the 1st ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. The eleven complete papers awarded have been conscientiously reviewed and chosen from 22 submissions. the 1st half makes a speciality of studying new representations and embeddings for time sequence class, clustering or for dimensionality relief.
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Extra info for Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers
Kernel methods are limited to positive semi-deﬁnite kernels and therefore can not be directly applied to general dissimilarity data. This limitation of kernel methods led to the dissimilarity representation introduced by Duin and co-workers [8,28]. Pairwise dissimilarities extend kernels to dyadic kernels [13,14,17], indeﬁnite kernels [11,16,18,23,27], and kernels on dissimilarity representations . Kernelizing indeﬁnite pairwise dissimilarity matrices by Eigenspectrum corrections is reviewed in .
Dimension reduction: perform dimension reduction in the dissimilarity space. There are numerous strategies for prototype selection. Naive examples include all elements of the training set X and sampling a random subset of X . For more sophisticated selection methods, we refer to . It is important to note that the prototypes need not to be elements of the training set. For example, one can use class means of time series as prototypes. For algorithms that compute a mean of a sample of time series, we refer to .
Syst. 39, 287–315 (2012) 19. : Time series classiﬁcation with ensembles of elastic distance measures. Data Min. Knowl. Dis. 29(3), 565–592 (2014) 20. : Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, pp. 1150–1157 (1999) 21. : Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004) 22. : Fisher kernels on visual vocabularies for image categorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.
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