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In the context of the highly increasing number of features that are availablenowadays we design a robust and fast method for feature selection. The methodtries to select the most representative features that are independent from eachother, but are strong together. We propose an algorithm that requires verylimited labeled data (as few as one labeled frame per class) and canaccommodate as many unlabeled samples. We also present here the supervisedapproach from which we started. We compare our two formulations withestablished methods like AdaBoost, SVM, Lasso, Elastic Net and FoBa and showthat our method is much faster and it has constant training time. Moreover, theunsupervised approach outperforms all the methods with which we compared andthe difference might be quite prominent. The supervised approach is in mostcases better than the other methods, especially when the number of trainingshots is very limited. All that the algorithm needs is to choose from a pool ofpositively correlated features. The methods are evaluated on theYoutube-Objects dataset of videos and on MNIST digits dataset, while attraining time we also used features obtained on CIFAR10 dataset and otherspre-trained on ImageNet dataset. Thereby, we also proved that transfer learningis useful, even though the datasets differ very much: from low-resolutioncentered images from 10 classes, to high-resolution images with objects from1000 classes occurring in different regions of the images or to very difficultvideos with very high intraclass variance. 7

Minimally Supervised Feature Selection for Classification (Master's Thesis, University Politehnica of Bucharest)