Optimizing feature extraction for multiclass cases
Chulhee Lee, Joonyong Hong · 2002
In this paper, we propose a new optimization method for multiclass feature extraction problems by assigning weights to each class in computing the global criterion function and adjusting the weights as new features are extracted. Recently, it is shown that it is possible to predict the classification error within 1-2% margin from the Bhattacharyya distance. We use the error prediction technique to adjust the weights of each classes. Initially, we assign equal weights to each class. After the first feature is extracted, we calculate classification error of each class when the first feature is used and adjust the weights accordingly. We compute again the global criterion function with a new set of weights excluding the first feature and calculate the second feature from the revised criterion function, and so on. Preliminary experiments show improvement over the conventional methods.