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Matrix factorization under contamination / Peter Ballen.
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Ballen, Peter, author.
- Language:
- English
- Subjects (All):
- Computer science.
- Statistics.
- Computer and Information Science--Penn dissertations.
- Penn dissertations--Computer and Information Science.
- Local Subjects:
- Computer science.
- Statistics.
- Computer and Information Science--Penn dissertations.
- Penn dissertations--Computer and Information Science.
- Genre:
- Academic theses.
- Physical Description:
- 1 online resource (122 pages)
- Contained In:
- Dissertations Abstracts International 82-04B.
- Place of Publication:
- [Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2020.
- Language Note:
- English
- System Details:
- Mode of access: World Wide Web.
- text file
- Summary:
- In the nonnegative matrix factorization problem, the user inputs a nonnegative matrix V and wants to factor V ≈ WH, with both W and H nonnegative. Standard factorization techniques make unrealistic assumptions about the noise present in the data: that the noise is generated from independent and identically distributed Gaussian process. However, real world datasets are unlikely to satisfy this simplistic assumption. In particular, real world datasets suffer from contamination, anomalies, and outliers that cannot be modeled by simple Gaussian distributions. In this dissertation, we discuss novel techniques for matrix factorization under contamination and non-standard noise models. These techniques can be used both as a replacement for a standard factorization algorithm, or as an independent contamination detection procedure. We also prove a number of complexity bounds on the hardness of the problem.
- Notes:
- Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
- Advisors: Roth, Aaron; Committee members: Anyndia De; Brett Falk; Edgar Dobriban; Sudipto Guha.
- Department: Computer and Information Science.
- Ph.D. University of Pennsylvania 2020.
- Local Notes:
- School code: 0175
- ISBN:
- 9798672164168
- Access Restriction:
- Restricted for use by site license.
- This item must not be sold to any third party vendors.
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