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Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series
Change detection in multivariate time series has applications in many domains, including health care and network monitoring.
Flexibly Mining Better Subgroups
For nominal attributes, this is relatively straightforward, as we can consider individual attribute values as binary features.
Canonical Divergence Analysis
We aim to analyze the relation between two random vectors that may potentially have both different number of attributes as well as realizations, and which may even not have a joint distribution.
Universal Dependency Analysis
For practical use, such a measure should be universal in the sense that it captures correlation in subspaces of any dimensionality and allows to meaningfully compare correlation scores across different subspaces, regardless how many dimensions they have and what specific statistical properties their dimensions possess.
