Deep networks such as autoencoders and deep belief nets are able to construct alternative, and often informative, representations of unlabeled data by searching for (hidden) structure and correlations between the features chosen to represent the data and combining them into new features that allow sparse representations of the data. These representations have been chosen to often increase the accuracy of further classification or regression accuracy when compared to the original, often human chosen representations. In this work, we attempt an investigation of the relation between such discovered representations found using related but differently represented sets of examples. To this end, we combine the cross-domain comparison capabilities of unsupervised manifold alignment with the unsupervised feature construction of deep belief nets, resulting in an example mapping function that allows re-encoding examples from any source to any target task. Using the t-Distributed Stochastic Neighbour Embedding technique to map translated and real examples to a lower dimensional space, we employ KL-divergence to define a dissimilarity measure between data sets enabling us to measure found representation similarities between domains.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com