Data smashing: uncovering lurking order in data.

ABSTRACT: From automatic speech recognition to discovering unusual stars, underlying almost all automated discovery tasks is the ability to compare and contrast data streams with each other, to identify connections and spot outliers. Despite the prevalence of data, however, automated methods are not keeping pace. A key bottleneck is that most data comparison algorithms today rely on a human expert to specify what 'features' of the data are relevant for comparison. Here, we propose a new principle for estimating the similarity between the sources of arbitrary data streams, using neither domain knowledge nor learning. We demonstrate the application of this principle to the analysis of data from a number of real-world challenging problems, including the disambiguation of electro-encephalograph patterns

SUBMITTER: Chattopadhyay I 

PROVIDER: S-EPMC4223903 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature