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In information retrieval, efficient accomplishing the nearest neighbor search on large scale database is a great challenge. Hashing based indexing methods represent each data instance as a binary string to retrieve the approximate nearest neighbors. In this paper, we present a semi-randomized hashing approach to preserve the Euclidean distance by binary codes. Euclidean distance preserving is a classic...
The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each...
Existing high-rate full diversity space-time codes (STC) always have high decoding complexity with traditional ML (maximum-likelihood) or near-ML decoding. Hence the decoding scheme with reduced complexity is required. In this paper, block-orthogonal structure is introduced for the first time, and most of the existing STC do have such structure. A simplified decoding is proposed to exploit the block-orthogonal...
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