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SqueezeFit Linear Program: Fast and Robust Label-aware Dimensionality Reduction

Lu, Tien-hsin
http://orcid.org/0000-0001-5629-072X
http://rave.ohiolink.edu/etdc/view?acc_num=osu1587156777565173

Abstract Details

2020, Master of Mathematical Sciences, Ohio State University, Mathematical Sciences.
We introduce the SqueezeFit linear program as a fast and robust dimensionality reduction method. This program is inspired by both the SqueezeFit semi-definite program [10] and scGeneFit [3], which is a linear program version of SqueezeFit that has been used to classify single cell RNA-sequence data with a given structured partition. The original SqueezeFit semi-definite program has a strong theoretical background but it exhibits slow runtimes with large data sets. In contrast, scGeneFit performs efficiently and robustly with scRNA-seq data given either flat or hierarchical label partitions, but it does not have much theoretical justification for its performance. The SqueezeFit linear program fills this computational and theoretical gap. After providing new theoretical guarantees, we illustrate the performance of the SqueezeFit linear program on real-world gene expression data.
Dustin G. Mixon, Dr. (Advisor)
Dongbin Xiu, Dr. (Committee Member)
52 p.
Mathematics
Dimensionality reduction; linear program; marker gene selection; machine learning; mathematical data science; optimization

Recommended Citations

Citations

  • Lu, T.-H. (2020). SqueezeFit Linear Program: Fast and Robust Label-aware Dimensionality Reduction [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587156777565173

    APA Style (7th edition)

  • Lu, Tien-hsin. SqueezeFit Linear Program: Fast and Robust Label-aware Dimensionality Reduction. 2020. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1587156777565173.

    MLA Style (8th edition)

  • Lu, Tien-hsin. "SqueezeFit Linear Program: Fast and Robust Label-aware Dimensionality Reduction." Master's thesis, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587156777565173

    Chicago Manual of Style (17th edition)

osu1587156777565173
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