This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations.
Jun 1, 2023 · This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations.
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The proposed methodology is able to approximate multivariate boundaries that can be used to separate spurious discoveries from statistically significant ones.
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Tensor data analysis allows researchers to uncover novel patterns and relationships that cannot be obtained from tabular data alone.
Tensor data analysis allows researchers to uncover novel patterns and relationships that cannot be obtained from tabular data alone.
Jun 2, 2023 · Results show that in some of the tested data, execution times yield statistically significant improvements when variables are clustered together ...
Dec 28, 2023 · Triclustering approaches generally fail to assess or guarantee the statistical significance of the retrieved patterns (i.e. whether they do not.
Read "TriSig: Evaluating the statistical significance of triclusters, Pattern Recognition" on DeepDyve, the largest online rental service for scholarly ...
The information inferred from multi-way patterns can offer valuable insights into disease progression, bioproduction processes, behavioral responses, weather ...
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A comprehensive overview of biclustering is presented, proposing an updated taxonomy for its fundamental components (bicluster, biclustering solution, ...