Rich Component Analysis
By Rong Ge, James Y. Zou. 2015
17 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Mathematics, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Abubakar Abid
- James Y. Zou
- Vivek Kumar Bagaria
- Martin Jinye Zhang
- Noah A. Smith
- Sunipa Dev
- Weixin Liang
- Laura Rieger
In Computational Mathematics
In Signal Processing
Citations in the corpus
- Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “See also Ge & Zou (2016) for a related approach.”
- Exploring patterns enriched in a dataset with contrastive principal component analysis (2018, methodology): “In a speci fi c application domain, there may be specialized tools in that domain with similar goals as cPCA 18 – 20 .”