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上传时间: 2013-10-18
详细说明：Semantic hashing[1] seeks compact binary codes of data-points so that the
Hamming distance between codewords correlates with semantic similarity.
In this paper, we show that the problem of finding a best code for a given
dataset is closely related to the problem of graph partitioning and can
be shown to be NP hard. By relaxing the original problem, we obtain a
spectral method whose solutions are simply a subset of thresholded eigen-
vectors of the graph Laplacian. By utilizing recent results on convergence
of graph Laplacian eigenvectors to the Laplace-Beltrami eigenfunctions of
manifolds, we show how to efficiently calculate the code of a novel data-
point. Taken together, both learning the code and applying it to a novel
point are extremely simple. Our experiments show that our codes outper-
form the state-of-the art.
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