Name
Abstract | ||
|---|---|---|
Multi-view learning leverages correlations between different sources of data to make predictions in one view based on observations in another view. A popular approach is to assume that, both, the correlations between the views and the view-specific covariances have a low-rank structure, leading to inter-battery factor analysis, a model closely related to canonical correlation analysis. We propose a convex relaxation of this model using structured norm regularization. Further, we extend the convex formulation to a robust version by adding an l1-penalized matrix to our estimator, similarly to convex robust PCA. We develop and compare scalable algorithms for several convex multi-view models. We show experimentally that the view-specific correlations are improving data imputation performances, as well as labeling accuracy in real-world multi-label prediction tasks. |
Year | Venue | DocType |
|---|---|---|
2014 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1404.6163 | 1 | 0.39 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Behrouz Behmardi | 1 | 8 | 2.60 |
| Cédric Archambeau | 2 | 618 | 46.02 |
| Guillaume Bouchard | 3 | 433 | 39.20 |