Abstract
We investigate a new notion of embedding of subsets of {-1, 1}n in a given normed space, in a way which preserves the structure of the given set as a class of functions on {1, ..., n}. This notion is an extension of the margin parameter often used in Nonparametric Statistics. Our main result is that even when considering "small" subsets of {-1, 1}n, the vast majority of such sets do not embed in a better way than the entire cube in any normed space that satisfies a minor structural assumption.
| Original language | English |
|---|---|
| Pages (from-to) | 25-45 |
| Number of pages | 21 |
| Journal | Random Structures and Algorithms |
| Volume | 27 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Aug 2005 |
Fingerprint
Dive into the research topics of 'Embedding with a Lipschitz function'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver