2 options
Fast linear algorithms for machine learning / Lu, Yichao.
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Lu, Yichao, author.
- Language:
- English
- Subjects (All):
- Statistics.
- Computer science.
- Applied Mathematics and Computational Science--Penn dissertations.
- Penn dissertations--Applied Mathematics and Computational Science.
- Local Subjects:
- Statistics.
- Computer science.
- Applied Mathematics and Computational Science--Penn dissertations.
- Penn dissertations--Applied Mathematics and Computational Science.
- Genre:
- Academic theses.
- Physical Description:
- 1 online resource (108 pages)
- Contained In:
- Dissertation Abstracts International 76-11B(E).
- Place of Publication:
- [Philadelphia, Pennsylvania]: University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2015.
- Language Note:
- English
- System Details:
- Mode of access: World Wide Web.
- text file
- Summary:
- Nowadays linear methods like Regression, Principal Component Analysis and Canonical Correlation Analysis are well understood and widely used by the machine learning community for predictive modeling and feature generation. Generally speaking, all these methods aim at capturing interesting subspaces in the original high dimensional feature space. Due to the simple linear structures, these methods all have a closed form solution which makes computation and theoretical analysis very easy for small datasets. However, in modern machine learning problems it's very common for a dataset to have millions or billions of features and samples. In these cases, pursuing the closed form solution for these linear methods can be extremely slow since it requires multiplying two huge matrices and computing inverse, inverse square root, QR decomposition or Singular Value Decomposition (SVD) of huge matrices. In this thesis, we consider three fast algorithms for computing Regression and Canonical Correlation Analysis approximate for huge datasets.
- Notes:
- Source: Dissertation Abstracts International, Volume: 76-11(E), Section: B.
- Advisors: Dean P. Foster; Committee members: Zongming Ma; Lyle H. Ungar.
- Department: Applied Mathematics and Computational Science.
- Ph.D. University of Pennsylvania 2015.
- Local Notes:
- School code: 0175
- ISBN:
- 9781321851397
- Access Restriction:
- Restricted for use by site license.
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