High-Dimensional Probability: An Introduction with Applications in Data Science

Clicks: 1
ID: 288150
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #1,170 of 1,244 articles by views in ADVANCES IN ARCHAEOLOGICAL PRACTICE

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,244 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.
Reference Key
persistent_1761420394_68fd246ad651d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Roman Vershynin
Journal ADVANCES IN ARCHAEOLOGICAL PRACTICE
Year 2018
DOI
10.1017/9781108231596
URL
https://www.cambridge.org/core/books/highdimensional-probability/797C466DA29743D2C8213493BD2D2102
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.