Semantic hashing |
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Authors: | Ruslan Salakhutdinov Geoffrey Hinton |
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Institution: | aDepartment of Computer Science, University of Toronto, 6 King’s College Road, Toronto, Ontario, Canada M5S 3G4 |
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Abstract: | We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than Latent Semantic Analysis. When the deepest layer is forced to use a small number of binary variables (e.g. 32), the graphical model performs “semantic hashing”: Documents are mapped to memory addresses in such a way that semantically similar documents are located at nearby addresses. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method. By using semantic hashing to filter the documents given to TF-IDF, we achieve higher accuracy than applying TF-IDF to the entire document set. |
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Keywords: | Information retrieval Graphical models Unsupervised learning |
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