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A Data Driven Similarity Measure and Example Mapping Function for General, Unlabelled Data Sets

机译:常规的数据驱动相似度测量和示例映射函数,未标记的数据集

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Deep networks such as autoencoders and deep belief nets are able to construct alternative, and often informative, repre-sentations 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 de-fine a dissimilarity measure between data sets enabling us to measure found representation similarities between domains.
机译:深网络,诸如自动编码和深信念网能够通过之间的特征选择代表数据搜索(隐藏)的结构和相关性并把它们组合成新的特征,其允许构造可替换的,并且经常信息的,未标记数据的repre-sentations数据的稀疏表示。与原始的人类所选择的表示相比,已选择这些表示通常会增加进一步分类或回归精度的准确性。在这项工作中,我们尝试调查使用相关但不同代表的例子的使用而发现的这种发现的表现之间的关系。为此,我们将无监督歧管对齐的跨域比较能力与深度信念网的无监督特征构造相结合,导致允许从任何源重新编码示例到任何目标任务的示例映射函数。使用T分布式随机邻居嵌入技术将翻译和实际示例映射到较低的维度空间,我们使用KL分歧来解决数据集之间的不相似性测量,使我们能够测量域之间找到的表示相似性。

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