Classification and Boosting with Multiple Collaborative Representations
Recent advances have shown a great potential to explore collaborative representations of test samples in a dictionary composed of training samples from all classes in multi-class recognition including sparse representations. In this paper, we present two
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|01_Chi_Classification_and_Boosting_2014.pdf||1.88 MB||Adobe PDF||Request a copy|
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