Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface

Uurtio, Viivi; Bomberg, Malin; Nybo, Kristian; Itävaara, Merja; Rousu, Juho

doi:10.1007/978-3-319-24282-8_25

Cited by 2 publications

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References 16 publications

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“…using the implementation proposed in [Uurtio et al 2015]. As stated in [Hardoon and Shawe-Taylor 2011], to determine which variable in the data space view X a is most related to the observation in K b , the algorithm needs to be run for all possible values of k. This means that every observation is in turn set as a basis for comparison and a sparse set of the remaining observations β is computed.…”

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confidence: 99%

A Tutorial on Canonical Correlation Methods

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Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has for instance been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.

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confidence: 99%

A Tutorial on Canonical Correlation Methods

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Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

Uurtio

Bhadra

Rousu

2018

2018 IEEE International Conference on Data Mining (ICDM)

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We have had a pleasant and inspiring working atmosphere in the group through all these years. In research, we do not always succeed in finding the most optimal solution and it requires new efforts to solve the problem differently. I would like to acknowledge Tom Petty and the Heartbreakers, the Rolling Stones, Ramones, Status Quo, and Eppu Normaali for producing entertaining music to overcome the unsuccessful moments. Finally, I give my warmest and most heartfelt thanks to my dear husband Rolle and our lovely daughter Rebecca, who make the sun shine on every single day of my life.

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Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface

Cited by 2 publications

References 16 publications

A Tutorial on Canonical Correlation Methods

A Tutorial on Canonical Correlation Methods

Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion

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