Small gestures go a long way: how many bits per gesture do recognizers actually need?
Proceedings of the Designing Interactive Systems Conference
MDL-Based analysis of time series at multiple time-scales
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
The impact of motion dimensionality and bit cardinality on the design of 3D gesture recognizers
International Journal of Human-Computer Studies
CID: an efficient complexity-invariant distance for time series
Data Mining and Knowledge Discovery
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Most algorithms for mining or indexing time series data do not operate directly on the original data, but instead they consider alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this paper, we investigate techniques to discover the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important sub-routine in algorithms for classification, clustering and outlier discovery. We will frame the discovery of these intrinsic features in the Minimal Description Length (MDL) framework. Extensive empirical tests show that our method is simpler, more general and significantly more accurate than previous methods, and has the important advantage of being essentially parameter-free.