Unlabeled detection is an emerging paradigm for modern decentralized decision systems faced with big-data applications, and for all those applications in which data must be fused without exploiting their identity, due to the lack of provenance labels, or to uncontrolled data shuffling Our focus here is on binary alphabets, and we ask: If our data have been shuffled in an unknown way, can a reliable decision about the underlying state of nature be made? Should the decision be made after an attempt to estimate the lost labels? And do there exist easily implementable decision rules? In answering these questions, we gain much insight: We show that two greedy algorithms previously introduced in the literature are equivalent to the GLRT, whose performance can be quite poor, and the detector known as ULR is equivalent to a simple counting rule. A new detector based on the central limit theorem is simply implementable and offers close-to-optimal performance in many scenarios of practical interest.

MAKING DECISIONS WITH SHUFFLED BITS

Marano, S;
2019-01-01

Abstract

Unlabeled detection is an emerging paradigm for modern decentralized decision systems faced with big-data applications, and for all those applications in which data must be fused without exploiting their identity, due to the lack of provenance labels, or to uncontrolled data shuffling Our focus here is on binary alphabets, and we ask: If our data have been shuffled in an unknown way, can a reliable decision about the underlying state of nature be made? Should the decision be made after an attempt to estimate the lost labels? And do there exist easily implementable decision rules? In answering these questions, we gain much insight: We show that two greedy algorithms previously introduced in the literature are equivalent to the GLRT, whose performance can be quite poor, and the detector known as ULR is equivalent to a simple counting rule. A new detector based on the central limit theorem is simply implementable and offers close-to-optimal performance in many scenarios of practical interest.
2019
978-1-4799-8131-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4738894
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