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Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines
2013
International Conference on Machine Learning
Unsupervised feature learning has emerged as a promising tool in learning representations from unlabeled data. However, it is still challenging to learn useful high-level features when the data contains a significant amount of irrelevant patterns. Although feature selection can be used for such complex data, it may fail when we have to build a learning system from scratch (i.e., starting from the lack of useful raw features). To address this problem, we propose a point-wise gated Boltzmann
dblp:conf/icml/SohnZLL13
fatcat:4xcsch3op5ahjndycwqr5bsvxu