Sparse on-line Gaussian processes
Neural Computation
Joint classifier and feature optimization for cancer diagnosis using gene expression data
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
| Hi-index | 0.01 |
Methods for learning sparse classification are among the state-of-the-art in supervised learning. Sparsity, essential to achieve good generalization capabilities, can be enforced by using heavy tailed priors/ regularizers on the weights of the linear combination of functions. These priors/regularizers favour a few large weights and many to exactly zero. The Sparse Multinomial Logistic Regression algorithm [1] is one of such methods, that adopts a Laplacian prior to enforce sparseness. Its applicability to large datasets is still a delicate task from the computational point of view, sometimes even impossible to perform. This work implements an iterative procedure to calculate the weights of the decision function that is O(m2) faster than the original method introduced in [1] (m is the number of classes). The benchmark dataset Indian Pines is used to test this modification. Results over subsets of this dataset are presented and compared with others computed with support vector machines.