| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1155229 | Statistics & Probability Letters | 2008 | 10 Pages |
Abstract
A popular method for unsupervised classification of high-dimensional data via decision trees is characterized as minimizing the empirical estimate of a concave information functional. It is shown that minimization of such functionals under the true distributions leads to perfect classification.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Damianos Karakos, Sanjeev Khudanpur, David J. Marchette, Adrian Papamarcou, Carey E. Priebe,