Improving activity discovery with automatic neighborhood estimation
IJCAI(2007)
摘要
A fundamental problem for artificial intelligence is identifying perceptual primitives from raw sensory signals that are useful for higher-level reasoning. We equate these primitives with initially unknown recurring patterns called motifs. Autonomously learning the motifs is difficult because their number, location, length, and shape are all unknown. Furthermore, nonlinear temporal warping may be required to ensure the similarity of motif occurrences. In this paper, we extend a leading motif discovery algorithm by allowing it to operate on multidimensional sensor data, incorporating automatic parameter estimation, and providing for motif-specific similarity adaptation. We evaluate our algorithm on several data sets and show how our approach leads to faster real world discovery and more accurate motifs compared to other leading methods.
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关键词
automatic neighborhood estimation,accurate motif,artificial intelligence,leading method,improving activity discovery,motif-specific similarity adaptation,leading motif discovery algorithm,automatic parameter estimation,multidimensional sensor data,real world discovery,motif occurrence,parameter estimation,artificial intelligent
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