Meta-Parameter Free Unsupervised Sparse Feature Learning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 26353006.
- Also identified by DOI 10.1109/TPAMI.2014.2366129.
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Abstract
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.