Meta-Parameter Free Unsupervised Sparse Feature Learning
Adriana Romero, Petia I. Radeva, Carlo Gatta · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2014
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL-10 and UCMerced show that the method achieves the state-of-the-art performance, providing discriminative features that generalize well.