Name
Title | ||
|---|---|---|
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares. |
Abstract | ||
|---|---|---|
Partial Least Squares (PLS) is a ubiquitous statistical technique for bilinear factor analysis. It is used in many analysis, machine learning, and information retrieval applications to model the covariance structure between a pair of matrices. In this paper, we consider PLS for representation learning in a multiview setting where we have more than one view in at training time. Furthermore, instead of framing PLS as a problem about a fixed given set, we argue that PLS should be studied as a stochastic optimization problem, especially in a big data setting, with the goal of optimizing a population objective based on sample. This view suggests using Stochastic Approximation (SA) approaches, such as Stochastic Gradient Descent (SGD) and enables a rigorous analysis of their benefits. In this paper, we develop SA approaches to PLS and provide iteration complexity bounds for the proposed algorithms. |
Year | Venue | Field |
|---|---|---|
2016 | ICML | Population,Stochastic gradient descent,Mathematical optimization,Stochastic optimization,Computer science,Partial least squares regression,Artificial intelligence,Stochastic approximation,Feature learning,Machine learning,Covariance,Bilinear interpolation |
DocType | Citations | PageRank |
Conference | 5 | 0.47 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Arora | 1 | 489 | 35.97 |
| Poorya Mianjy | 2 | 18 | 4.40 |
| Teodor Marinov | 3 | 7 | 3.54 |