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Principled Analysis of High-Dimensional Data: How to Identify Signals while under the Curse of Dimensionality

Description:

Modern data sets are increasingly vast, not only in the number of samples, but also in the number of measurements, or features, that they contain. This high-dimensionality poses a unique set of problems for data analysis due to a set of phenomena known as ``the curse of dimensionality.'' This thesis...

Keyword:

Data Visualization, Circular Linear Regression, Dimensionality Reduction, Curse of Dimensionality, and Quality Assessment

Subject:

Applied mathematics, Bioinformatics, and Computer science

Creator:

Johnson, Eric Mitchell

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

06/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_892588 and http://dissertations.umi.com/northwestern:15988
Discriminative Dimensionality Reduction using Deep Neural Networks

Description:

Deep neural networks have shown impressive performance for many applications. In this dissertation, leveraging the capabilities of neural networks for modeling the non-linearity exists in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The suggested models can transfer knowledge from...

Keyword:

Discriminative, Clustering, LIGO, and Dimensionality Reduction

Subject:

Computer science

Creator:

Bahaadini Beigy Zarandi, Sara

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/12/2020

Date Created:

2019-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:14850 and etdadmin_upload_686625

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Principled Analysis of High-Dimensional Data: How to Identify Signals while under the Curse of Dimensionality

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