Machine Learning Coffee seminar: "Finding Outlier Correlations" Petteri Kaski

Weekly seminars held jointly by Aalto University and the University of Helsinki.

Helsinki region machine learning researchers will start our week by an exciting machine learning talk. The aim is to gather people from different fields of science with interest in machine learning. Porridge and coffee is served at 9:00 and the talk will begin at 9:15.

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Finding Outlier Correlations

Petteri Kaski
Professor of Computer Science, Aalto University

Abstract:

Finding strongly correlated pairs of observables is one of the basic tasks in data analysis and machine learning. Assuming we have N observables, there are N(N-1)/2 pairs of distinct observables, which gives rise to quadratic scalability in N if our approach is to explicitly compute all pairwise correlations.

In this talk, we look at algorithm designs that achieve subquadratic scalability in N to find pairs of observables that are strongly correlated compared with the majority of the pairs. Our plan is to start with an exposition of G. Valiant's breakthrough design [FOCS'12,JACM'15] and then look at subsequent improved designs, including some of our own work.

Based on joint work with M. Karppa, J. Kohonen, and P. Ó Catháin, cf. https://arxiv.org/abs/1510.03895 (ACM TALG, to appear) and https://arxiv.org/abs/1606.05608 (ESA'16).

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