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Statistical Machine Learning Approaches in Photographic and Social Science Applications.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical Machine Learning Approaches in Photographic and Social Science Applications./
Author:	Wang, Justin Sijie.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	98 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13423688
ISBN:	9781392212363

Statistical Machine Learning Approaches in Photographic and Social Science Applications.
Wang, Justin Sijie.

Statistical Machine Learning Approaches in Photographic and Social Science Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 98 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2019.

This item must not be added to any third party search indexes.

We present contributions to the application areas of photography and social sciences using a statistical machine learning approach, as well as methodological work in dimension reduction techniques. We first present a machine-learning based photography project in which we analyzed patterns of rule break among high-quality photographs. While high-quality photographs generally adhere to high-level rules set forth in photography, some high-quality photographs purposefully bend the rules to achieve a greater effect. Our work in photography solidifies the patterns of conditions that allow for such bends in rules. To do so, we used a machine learning and image processing based approach to find and evaluate rule breaking photographs in order to discover the patterns that justify their rule breaking.Next, we present an analysis of whether gender bias exists in the field of statistics. Specifically, we quantify the differences in citation counts between men and women in the statistics field, using a sample of papers from the top statistics journals. We first identified the gender of the first author of each paper in our dataset. We also added important covariates such as job title and country of employment of the first author, in order to account for factors that may possibly affect or possibly explain any disparity in citation counts between men and women. Our analysis found that, controlling for covariates and replications, that there is no evidence to suggest that there is a citation disparity between men and women authors in statistics.We also developed a modification of the locally linear embedding (LLE) dimension reduction algorithm that is designed to handle additive noise. This new modification is termed LLEAN, short for locally linear embedding with additive noise, and has been shown to perform better in the presence of noise distortion. LLEAN seeks to recover the noiseless data from the noisy data by exploiting the relationship between local linearity and reconstruction potential. The recovered noiseless data is then used to perform the subsequent dimension reduction steps. Our work on LLEAN includes an automatic selection method for the tuning parameter to remove the burden from the user.

ISBN: 9781392212363Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Dimension reduction

Statistical Machine Learning Approaches in Photographic and Social Science Applications.
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245 1 0 $a Statistical Machine Learning Approaches in Photographic and Social Science Applications.
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300 $a 98 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Lee, Thomas.
502 $a Thesis (Ph.D.)--University of California, Davis, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a We present contributions to the application areas of photography and social sciences using a statistical machine learning approach, as well as methodological work in dimension reduction techniques. We first present a machine-learning based photography project in which we analyzed patterns of rule break among high-quality photographs. While high-quality photographs generally adhere to high-level rules set forth in photography, some high-quality photographs purposefully bend the rules to achieve a greater effect. Our work in photography solidifies the patterns of conditions that allow for such bends in rules. To do so, we used a machine learning and image processing based approach to find and evaluate rule breaking photographs in order to discover the patterns that justify their rule breaking.Next, we present an analysis of whether gender bias exists in the field of statistics. Specifically, we quantify the differences in citation counts between men and women in the statistics field, using a sample of papers from the top statistics journals. We first identified the gender of the first author of each paper in our dataset. We also added important covariates such as job title and country of employment of the first author, in order to account for factors that may possibly affect or possibly explain any disparity in citation counts between men and women. Our analysis found that, controlling for covariates and replications, that there is no evidence to suggest that there is a citation disparity between men and women authors in statistics.We also developed a modification of the locally linear embedding (LLE) dimension reduction algorithm that is designed to handle additive noise. This new modification is termed LLEAN, short for locally linear embedding with additive noise, and has been shown to perform better in the presence of noise distortion. LLEAN seeks to recover the noiseless data from the noisy data by exploiting the relationship between local linearity and reconstruction potential. The recovered noiseless data is then used to perform the subsequent dimension reduction steps. Our work on LLEAN includes an automatic selection method for the tuning parameter to remove the burden from the user.
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