Introduction to social computing
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Active learning of combinatorial features for interactive optimization
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
| Hi-index | 0.00 |
Zero-norm, defined as the number of non-zero elements in a vector, is an ideal quantity for feature selection. However, minimization of zero-norm is generally regarded as a combinatorially difficult optimization problem. In contrast to previous methods that usually optimize a surrogate of zero-norm, we propose a direct optimizationmethod to achieve zero-norm for feature selection in this paper. Based on Expectation Maximization (EM), this method boils down to solving a sequence of Quadratic Programming problems and hence can be practically optimized in polynomial time. We show that the proposed optimization technique has a nice Bayesian interpretation and converges to the true zero norm asymptotically, provided that agood starting point is given. Following the scheme of our proposed zero-norm, we even show that an arbitrary-norm based Support Vector Machine can be achieved in polynomial time. A series of experiments demonstrate that our proposed EM based zero-norm outperforms other state-of-the-art methods for feature selection on biological microarray data and UCI data, in terms of both the accuracy and the learning efficiency.