Topics in high-dimensional statistical learning
Abstract/Contents
- Abstract
- Our research explores three topics in high-dimensional statistical learning. First, we consider a regression scenario where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of l1 -regularized regression for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. We illustrate this idea on real and simulated data. We then consider regression scenarios where it is natural to allow coefficients to vary as smooth functions of other variables. We propose two constrained versions for this problem and show how to solve them efficiently. Last, we study canonical correlation analysis (CCA) in high-dimensional settings and propose a sparse CCA framework, and provide two efficient algorithms. We discuss links between CCA and linear discriminant analysis (LDA). We demonstrate its use on real and simulated data.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Suo, Xiaotong |
|---|---|
| Degree supervisor | Saunders, Michael A |
| Degree supervisor | Tibshirani, Robert |
| Thesis advisor | Saunders, Michael A |
| Thesis advisor | Tibshirani, Robert |
| Thesis advisor | Taylor, Jonathan E |
| Degree committee member | Taylor, Jonathan E |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xiaotong Suo. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Xiaotong Suo
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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