Name
Abstract | ||
|---|---|---|
Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonparametric version of these models can be recovered through the Gaussian process latent variable model. This gives us a flexible formalism for multi-view learning where the latent variables can be used both for exploratory purposes and for learning representations that enable efficient inference for ambiguous estimation tasks. Learning is performed in a Bayesian manner through the formulation of a variational compression scheme which gives a rigorous lower bound on the log likelihood. Our Bayesian framework provides strong regularization during training, allowing the structure of the latent space to be determined efficiently and automatically. We demonstrate this by producing the first (to our knowledge) published results of learning from dozens of views, even when data is scarce. We further show experimental results on several different types of multi-view data sets and for different kinds of tasks, including exploratory data analysis, generation, ambiguity modelling through latent priors and classification. |
Year | DOI | Venue |
|---|---|---|
2016 | v22/16-179.html | JOURNAL OF MACHINE LEARNING RESEARCH |
Keywords | Field | DocType |
representation learning, factor analysis, Gaussian processes, inter-battery factor analysis | Inference,Latent class model,Latent variable,Artificial intelligence,Probabilistic latent semantic analysis,Exploratory data analysis,Prior probability,Feature learning,Machine learning,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
22 | 1 | 1532-4435 |
Citations | PageRank | References |
1 | 0.63 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| andreas damianou | 1 | 151 | 17.68 |
| Neil D. Lawrence | 2 | 3411 | 268.51 |
| carl henrik ek | 3 | 327 | 30.76 |