Removing biases in unsupervised learning of sequential patterns

9 years 11 months ago
Removing biases in unsupervised learning of sequential patterns
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the data. Popular approaches to such learning include (and often combine) frequency-based approaches and statistical analysis. However, the quality of results is often far from satisfactory. Though most previous investigations seek to address method-specific limitations, we instead focus on general (methodneutral) limitations in current approaches. This paper takes two key steps towards addressing such general quality-reducing flaws. First, we carry out an in-depth empirical comparison and analysis of popular sequence learning methods in terms of the quality of information produced, for several synthetic and real-world datasets, under controlled settings of noise. We find that both frequency-based and statisticsbased approaches (i) suffer from common statistical biases based on the length of the sequences co...
Yoav Horman, Gal A. Kaminka
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2007
Where IDA
Authors Yoav Horman, Gal A. Kaminka
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