1,914 research outputs found
Expurgated Bounds for the Asymmetric Broadcast Channel
This work contains two main contributions concerning the expurgation of
hierarchical ensembles for the asymmetric broadcast channel. The first is an
analysis of the optimal maximum likelihood (ML) decoders for the weak and
strong user. Two different methods of code expurgation will be used, that will
provide two competing error exponents. The second is the derivation of
expurgated exponents under the generalized stochastic likelihood decoder (GLD).
We prove that the GLD exponents are at least as tight as the maximum between
the random coding error exponents derived in an earlier work by Averbuch and
Merhav (2017) and one of our ML-based expurgated exponents. By that, we
actually prove the existence of hierarchical codebooks that achieve the best of
the random coding exponent and the expurgated exponent simultaneously for both
users
Similarity Search Over Graphs Using Localized Spectral Analysis
This paper provides a new similarity detection algorithm. Given an input set
of multi-dimensional data points, where each data point is assumed to be
multi-dimensional, and an additional reference data point for similarity
finding, the algorithm uses kernel method that embeds the data points into a
low dimensional manifold. Unlike other kernel methods, which consider the
entire data for the embedding, our method selects a specific set of kernel
eigenvectors. The eigenvectors are chosen to separate between the data points
and the reference data point so that similar data points can be easily
identified as being distinct from most of the members in the dataset.Comment: Published in SampTA 201
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