@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Martha White's publication directory www.cs.ualberta.ca/~whitem/research
@InProceedings(12nips-multiview,
  Title = "Convex Multi-view Subspace Learning",
  Author = "Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans",
  Abstract="Subspace learning seeks a low dimensional representation of data
that enables accurate reconstruction.
However, in many applications, data is obtained
from multiple sources rather than a single source
(e.g. an object might be viewed by cameras at different angles,
or a document might consist of text and images).
The conditional independence of separate sources imposes constraints on
their shared latent representation, which, if respected, can
improve the quality of the learned low dimensional
representation.
In this paper, we present a convex formulation of multi-view subspace
learning that enforces conditional independence while reducing dimensionality.
For this formulation, we develop an efficient algorithm that
recovers an optimal data reconstruction by exploiting an implicit
convex regularizer, then recovers the corresponding latent representation
and reconstruction model, jointly and optimally.
Experiments illustrate that the proposed method produces high quality results.",
  Year = "2012",
  Booktitle = "Advances in Neural Information Processing Systems (NIPS)",
  AcceptRate = "25.2\%",
  AcceptNumbers = "370 of 1467"
  bib2html_dl_pdf="../publications/12nips-multiview.pdf"
)