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PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES | IEEE Conference Publication | IEEE Xplore

PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES


Abstract:

The task of similarity identification is to identify items in a database which are similar to a given query item for a given metric. The identification rate of a compress...Show More

Abstract:

The task of similarity identification is to identify items in a database which are similar to a given query item for a given metric. The identification rate of a compression scheme characterizes the minimum rate that can be achieved which guarantees reliable answers with respect to a given similarity threshold [1]. In this paper, we study a prediction-based quadratic similarity identification for autoregressive processes. We use an ideal linear predictor to remove linear dependencies in autoregressive processes. The similarity identification is conducted on the residuals. We show that the relation between the distortion of query and database processes and the distortion of their residuals is characterized by a sequence of eigenvalues. We derive the identification rate of our prediction-based approach for autoregressive Gaussian processes. We characterize the identification rate for the special case where only the smallest value in the sequence of eigenvalues is required to be known and derive its analytical upper bound by approximating a sequence of matrices with a sequence of Toeplitz matrices.
Date of Conference: 26-29 November 2018
Date Added to IEEE Xplore: 21 February 2019
ISBN Information:
Conference Location: Anaheim, CA, USA

References

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