  In the context of the highly increasing number of features that are available
nowadays we design a robust and fast method for feature selection. The method
tries to select the most representative features that are independent from each
other, but are strong together. We propose an algorithm that requires very
limited labeled data (as few as one labeled frame per class) and can
accommodate as many unlabeled samples. We also present here the supervised
approach from which we started. We compare our two formulations with
established methods like AdaBoost, SVM, Lasso, Elastic Net and FoBa and show
that our method is much faster and it has constant training time. Moreover, the
unsupervised approach outperforms all the methods with which we compared and
the difference might be quite prominent. The supervised approach is in most
cases better than the other methods, especially when the number of training
shots is very limited. All that the algorithm needs is to choose from a pool of
positively correlated features. The methods are evaluated on the
Youtube-Objects dataset of videos and on MNIST digits dataset, while at
training time we also used features obtained on CIFAR10 dataset and others
pre-trained on ImageNet dataset. Thereby, we also proved that transfer learning
is useful, even though the datasets differ very much: from low-resolution
centered images from 10 classes, to high-resolution images with objects from
1000 classes occurring in different regions of the images or to very difficult
videos with very high intraclass variance. 7
