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Rich component analysis

Publication ,  Conference
Ge, R; Zou, J
Published in: 33rd International Conference on Machine Learning, ICML 2016
January 1, 2016

In many settings, we have multiple data sets (also called views) that capture different and overlapping aspects of the same phenomenon. We are often interested in finding patterns that are unique to one or to a subset of the views. For example, we might have one set of molecular observations and one set of physiological observations on the same group of individuals, and we want to quantify molecular patterns that are uncorrelated with physiology. Despite being a common problem, this is highly challenging when the correlations come from complex distributions. In this paper, we develop the general framework of Rich Component Analysis (RCA) to model settings where the observations from different views are driven by different sets of latent components, and each component can be a complex, high-dimensional distribution. We introduce algorithms based on cumulant extraction that provably learn each of the components without having to model the other components. We show how to integrate RCA with stochastic gradient descent into a meta-algorithm for learning general models, and demonstrate substantial improvement in accuracy on several synthetic and real datasets in both supervised and unsupervised tasks. Our method makes it possible to learn latent variable models when we don't have samples from the true model but only samples after complex perturbations.

Duke Scholars

Published In

33rd International Conference on Machine Learning, ICML 2016

ISBN

9781510829008

Publication Date

January 1, 2016

Volume

3

Start / End Page

2238 / 2255
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ge, R., & Zou, J. (2016). Rich component analysis. In 33rd International Conference on Machine Learning, ICML 2016 (Vol. 3, pp. 2238–2255).
Ge, R., and J. Zou. “Rich component analysis.” In 33rd International Conference on Machine Learning, ICML 2016, 3:2238–55, 2016.
Ge R, Zou J. Rich component analysis. In: 33rd International Conference on Machine Learning, ICML 2016. 2016. p. 2238–55.
Ge, R., and J. Zou. “Rich component analysis.” 33rd International Conference on Machine Learning, ICML 2016, vol. 3, 2016, pp. 2238–55.
Ge R, Zou J. Rich component analysis. 33rd International Conference on Machine Learning, ICML 2016. 2016. p. 2238–2255.

Published In

33rd International Conference on Machine Learning, ICML 2016

ISBN

9781510829008

Publication Date

January 1, 2016

Volume

3

Start / End Page

2238 / 2255