With the rapid development of the mobile Internet, more and more diverse data come from mobile devices. These big data bring us challenges and chances on data processing and analysis. At the same time, with accelerated computing power and big data, Deep Learning has overcome its disadvantages such as overfitting. It has become a powerful tool to handle complex data on the Internet. While so many data are available, most of them are unlabeled and this brings a great need for unsupervised feature learning. This thesis takes the deep neural network as a tool and targets on the problem of unsupervised feature learning. We propose in this thesis two methods to do unsupervised feature learning with deep neural network. The first one is the deep embedding network for clustering, which makes use of local structure preservation and group sparsity to force the network to learn a clustering-oriented representation. The second one is the generalized auto-encoder which aims to learn a feature from the intrinsic manifold space of data by exploring the relation between data points and learning their representation iteratively.
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