PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mathematical Programming for Missing Data
Chiranjib Bhattacharyya, K.S. Pannagadatta and Alex Smola
In: NIPS, 17, 2004, Dec 2004, Canada.


We propose a mathematical programming method to deal with uncertainty in the observations of a classification problem. This means that we can deal with situations where instead of a sample $(\xb_i, y_i)$ we may only have a distribution over $(\xb_i, y_i)$ at our disposition. In particular, we derive a robust formulation when the uncertainty is given by a normal distribution. This leads to Second Order Cone Programming Problems. Our method can be applied to the problem of missing data, where it outperforms direct imputation.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:721
Deposited By:Adam Kowalczyk
Deposited On:02 January 2005