Example Selection For Dictionary Learning
This work addresses the efficiency of unsupervised learning for researchers, but it is incremental as it builds on existing dictionary learning methods.
The paper tackled the problem of accelerating dictionary learning by exploring active example selection strategies instead of uniform sampling, showing that some algorithms improve learning speed.
In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data. In this work, we explore whether active example selection strategies - algorithms that select which examples to use, based on the current estimate of the features - can accelerate learning. Specifically, we investigate effects of heuristic and saliency-inspired selection algorithms on the dictionary learning task with sparse activations. We show that some selection algorithms do improve the speed of learning, and we speculate on why they might work.