Northwestern University

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Filtering by: Creator Kim, Cheolmin Resource type Dissertation Subject Applied mathematics

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Optimization Methods for Scale Invariant Problems in Machine Learning

Description:

While optimization has received much attention in the machine learning community, most of them consider unconstrained supervised learning models such as neural networks and support vector machine. In this dissertation, we introduce a new class of optimization problems called scale invariant problems that include interesting unsupervised learning models such as...

Keyword:

Power Iteration, PCA, Optimization, Machine Learning, Scale Invariance, and KL-NMF

Subject:

Applied mathematics and Operations research

Creator:

Kim, Cheolmin

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/01/2021

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:15139 and etdadmin_upload_744178