Sparse Principal Component Analysis with Model order Reduction
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Sparse Principal Component Analysis with Model order Reduction
Alternative title
Authors
Published Date
2016-06
Publisher
Type
Thesis or Dissertation
Abstract
Principal Component Analysis (PCA) has become a standard tool for identification of the maximal variance in data. The directions of maximum variance provide very insightful information about the data in a lot of applications. By augmenting the PCA problem with a penalty term that promotes sparsity, we are able to obtain sparse vectors describing the direction of maximum variance in the data. A sparse vector becomes very useful in many applications like finance, where it has a direct impact on cost. An algorithm which computes principal component vector in in a reduced space by using model order reduction techniques and enforces sparsity in the full space is described in this work. We achieve computational savings by enforcing sparsity in different coordinates than those in which the principal components are computed. This is illustrated by applying the algorithm to synthetic data. The algorithm is also applied to the linearized Navier-Stokes equations for a plane channel flow.
Keywords
Description
University of Minnesota M.S.E.E. thesis. June 2016. Major: Electrical Engineering. Advisor: Mihailo Jovanovic. 1 computer file (PDF); v, 21 pages.
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Sivaraman, Prashanth Bharadwaj. (2016). Sparse Principal Component Analysis with Model order Reduction. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/182115.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.