Version 1
: Received: 1 February 2018 / Approved: 1 February 2018 / Online: 1 February 2018 (16:32:04 CET)
How to cite:
Pham, G.; Boyer, R.; Nielsen, F. Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors. Preprints2018, 2018020008. https://doi.org/10.20944/preprints201802.0008.v1
Pham, G.; Boyer, R.; Nielsen, F. Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors. Preprints 2018, 2018020008. https://doi.org/10.20944/preprints201802.0008.v1
Pham, G.; Boyer, R.; Nielsen, F. Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors. Preprints2018, 2018020008. https://doi.org/10.20944/preprints201802.0008.v1
APA Style
Pham, G., Boyer, R., & Nielsen, F. (2018). Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors. Preprints. https://doi.org/10.20944/preprints201802.0008.v1
Chicago/Turabian Style
Pham, G., Rémy Boyer and Frank Nielsen. 2018 "Computational Information Geometry For Binary Classification of High-Dimensional Random Tensors" Preprints. https://doi.org/10.20944/preprints201802.0008.v1
Abstract
The performance in terms of minimal Bayes’ error probability for detection of a high-dimensional random tensor is a fundamental under-studied difficult problem. In this work, we consider two Signal to Noise Ratio (SNR)-based detection problems of interest. Under the alternative hypothesis, i.e., for a non-zero SNR, the observed signals are either a noisy rank-R tensor admitting a Q-order Canonical Polyadic Decomposition (CPD) with large factors of size Nq R, i.e, for 1 q Q, where R, Nq ! ¥ with R1/q/Nq converge towards a finite constant or a noisy tensor admitting TucKer Decomposition (TKD) of multilinear (M1, . . . ,MQ)-rank with large factors of size Nq Mq, i.e, for 1 q Q, where Nq,Mq ! ¥ with Mq/Nq converge towards a finite constant. The detection of the random entries (coefficients) of the core tensor in the CPD/TKD is hard to study since the exact derivation of the error probability is mathematically intractable. To circumvent this technical difficulty, the Chernoff Upper Bound (CUB) for larger SNR and the Fisher information at low SNR are derived and studied, based on information geometry theory. The tightest CUB is reached for the value minimizing the error exponent, denoted by s?. In general, due to the asymmetry of the s-divergence, the Bhattacharyya Upper Bound (BUB) (that is, the Chernoff Information calculated at s? = 1/2) can not solve this problem effectively. As a consequence, we rely on a costly numerical optimization strategy to find s?. However, thanks to powerful random matrix theory tools, a simple analytical expression of s? is provided with respect to the Signal to Noise Ratio (SNR) in the two schemes considered. A main conclusion of this work is that the BUB is the tightest bound at low SNRs. This property is, however, no longer true for higher SNRs.
Keywords
Optimal Bayesian detection, information geometry, minimal error probability, Chernoff/Bhattacharyya upper bound, large random tensor, Fisher information, large random sensing matrix
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
