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Geometric Data Organization: Algorithms and Applications.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Geometric Data Organization: Algorithms and Applications./
作者:
Stanley, Jay S., III.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
164 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:
Dissertations Abstracts International82-12B.
標題:
Applied mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736931
ISBN:
9798516931055
Geometric Data Organization: Algorithms and Applications.
Stanley, Jay S., III.
Geometric Data Organization: Algorithms and Applications.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 164 p.
Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Thesis (Ph.D.)--Yale University, 2020.
This item must not be sold to any third party vendors.
High dimensional and high throughput technologies are central tools of modern scientific research. These tools cast a wide net with the goal of amassing sufficiently strong signals to detect subtle phenomena in complex systems. Yet, the complexity and size of modern data is precisely the crux of contemporary data science. Without processing, high dimensional data is indecipherable to a human; thus, a central goal of machine learning is to reduce massive datasets into interpretable, rich representations. Secondly, the immense scale of modern data poses a computational challenge that demands the development of efficient algorithms. In this thesis, we leverage the intrinsic geometry of data to simultaneously address challenges of high dimensionality and large sample datasets, developing representation learning algorithms that efficiently scale for large datasets. Towards the former, we construct algorithms for data exploration by augmenting data according to its geometry and characterizing the geometrically distribution of data points from different experimental conditions in the representation space. For the latter, we use data geometry to develop algorithms for learning compressed data representations that can be computed quickly as well as unified representations of large multi-experiment and multi-modality datasets. To demonstrate our approaches, we tackle general machine learning problems including classification, clustering, and regression. As an application domain, we particularly focus on single cell RNA sequencing (scRNA-seq) and also consider applications to single cell chromatin profiling (scATAC-seq) and mass cytometry (CyTOF).
ISBN: 9798516931055Subjects--Topical Terms:
2122814
Applied mathematics.
Subjects--Index Terms:
Algorithms
Geometric Data Organization: Algorithms and Applications.
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High dimensional and high throughput technologies are central tools of modern scientific research. These tools cast a wide net with the goal of amassing sufficiently strong signals to detect subtle phenomena in complex systems. Yet, the complexity and size of modern data is precisely the crux of contemporary data science. Without processing, high dimensional data is indecipherable to a human; thus, a central goal of machine learning is to reduce massive datasets into interpretable, rich representations. Secondly, the immense scale of modern data poses a computational challenge that demands the development of efficient algorithms. In this thesis, we leverage the intrinsic geometry of data to simultaneously address challenges of high dimensionality and large sample datasets, developing representation learning algorithms that efficiently scale for large datasets. Towards the former, we construct algorithms for data exploration by augmenting data according to its geometry and characterizing the geometrically distribution of data points from different experimental conditions in the representation space. For the latter, we use data geometry to develop algorithms for learning compressed data representations that can be computed quickly as well as unified representations of large multi-experiment and multi-modality datasets. To demonstrate our approaches, we tackle general machine learning problems including classification, clustering, and regression. As an application domain, we particularly focus on single cell RNA sequencing (scRNA-seq) and also consider applications to single cell chromatin profiling (scATAC-seq) and mass cytometry (CyTOF).
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