On Convex Dimensionality Reduction for Classification Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_projects/1n79h435t

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Dimensionality reduction (DR) is an efficient approach to reduce the size of data by capturing the informative intrinsic features and discarding the noise. DR methods can be grouped through a variety of categories, e.g. supervised/ unsupervised, linear/non-linear or parametric/non-parametric. Objective function based methods can be grouped into convex and non convex. Non convex methods have the tendency to converge to local optimal solutions which may differ from the desired global solution. To overcome this problem, one can frame a non convex problem as a convex problem. A direct transformation of a non convex formulation into a convex formulation is non trivial. In this project, we chose a non convex, linear, supervised, multi-class, non parametric DR method’s objective, which can be framed using convex formulation. We present the process of convex formulation, different methods of implementation, approaches to increase efficiency and numerical analysis results.
Resource Type
Date Available
Date Issued
Keyword
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2011-04-04T17:53:44Z (GMT). No. of bitstreams: 3 deepthi_project_report.pdf: 1486915 bytes, checksum: b39e0548bebd2dade02c9af0d9baa8ee (MD5) license_rdf: 19018 bytes, checksum: e925e67ba3f3a5eb5469c8aa195de64e (MD5) license_text: 20539 bytes, checksum: 1c474fa3b0dfbff784464c41b9d53728 (MD5)
  • description.provenance : Submitted by Tez Deepthi Tammineni (tamminet@onid.orst.edu) on 2011-04-04T17:53:44Z No. of bitstreams: 3 deepthi_project_report.pdf: 1486915 bytes, checksum: b39e0548bebd2dade02c9af0d9baa8ee (MD5) license_rdf: 19018 bytes, checksum: e925e67ba3f3a5eb5469c8aa195de64e (MD5) license_text: 20539 bytes, checksum: 1c474fa3b0dfbff784464c41b9d53728 (MD5)

Relationships

In Administrative Set:
Last modified: 07/05/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items