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A Probabilistic Detection Algorithm for Noiseless Group Testing

무잡음 그룹검사에 대한 확률적 검출 알고리즘

  • Seong, Jin-Taek (Department of Convergence Software, Mokpo National University)
  • Received : 2019.07.05
  • Accepted : 2019.07.29
  • Published : 2019.10.31

Abstract

This paper proposes a detection algorithm for group testing. Group testing is a problem of finding a very small number of defect samples out of a large number of samples, which is similar to the problem of Compressed Sensing. In this paper, we define a noiseless group testing and propose a probabilistic algorithm for detection of defective samples. The proposed algorithm is constructed such that the extrinsic probabilities between the input and output signals exchange with each other so that the posterior probability of the output signal is maximized. Then, defective samples are found in the group testing problem through a simulation on the detection algorithm. The simulation results for this study are compared with the lower bound in the information theory to see how much difference in failure probability over the input and output signal sizes.

본 논문은 그룹검사(Group Testing)에 대한 검출 알고리즘을 제안한다. 그룹검사는 다수의 샘플 중 극히 일부의 결함 샘플을 찾는 문제로써 이것은 압축센싱 문제와 유사하다. 본 논문에서는 잡음이 없는 그룹검사를 정의하고, 결함 샘플을 검출하기 위한 확률 기반의 알고리즘을 제안한다. 제안하는 알고리즘은 입력과 출력 신호 간 외부확률들이 서로 교환하여 출력 신호의 사후 확률이 최대가 되도록 구성한다. 그리고 검출 알고리즘에 대한 모의실험을 통해 그룹검사 문제에서 결함 샘플을 찾는다. 본 연구에 대한 모의시험 결과는 정보이론의 하한치와 비교하여 입력과 출력 신호 크기에 따라 실패확률이 얼마나 차이가 있는지 확인한다.

Keywords

Acknowledgement

This Research was supported by Research Funds of Mokpo National University in 2018.

References

  1. D. Robert, "The Detection of Defective Members of Large Populations," The Annals of Mathematical Statistics, vol. 14, no. 4, pp. 436-440, Dec. 1943. https://doi.org/10.1214/aoms/1177731363
  2. D. L. Donoho, "Compressed Sensing," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006. https://doi.org/10.1109/TIT.2006.871582
  3. V. Ganditota, E. Grigorescu. S. Jaggi, and S. Zhou, "Nearly Optimal Sparse Group Testing," IEEE Transactions on Information Theory, vol. 65, no. 5, pp. 2760-2773, May. 2019. https://doi.org/10.1109/TIT.2019.2891651
  4. D. Z. Du, and F. K. Hwang, Pooling Designs and Nonadaptive Group Testing: Important Tools for DNA Sequencing, World Scientific, 2006.
  5. C. L. Chan, P. H. Che, S. Jaggi, and V. Saligrama, "Non-adaptive probabilistic group testing with noisy measurements: near-optimal bounds with efficient algorithms," 49th Annual Allerton Conference on Communication, Control, and Computing, pp. 1832-1839, Sep. 2011.
  6. M. Aldridge, L. Baldassini, and O. Johnson, "Group Testing Algorithms: Bounds and Simulations," IEEE Transactions on Information Theory, vol. 60, no. 6, pp. 3671-3687, Jun. 2014. https://doi.org/10.1109/TIT.2014.2314472
  7. M. C. Davey, and D. Mackey, "Low-density parity-check codes over GF(q)," IEEE Communications Letters, vol. 2, no. 6, pp. 165-167, Jun. 1998. https://doi.org/10.1109/4234.681360
  8. M. Aldridge, "The Capacity of Bernoulli Nonadaptive Group Testing," IEEE Transactions on Information Theory, vol. 63, no. 11, pp. 7142-7148, Nov. 2017. https://doi.org/10.1109/TIT.2017.2748564
  9. J. Scarlett, and V. Cevher, "Near-Optimal Noisy Group Testing via Separate Decoding of Items," IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 5, pp. 902-915, Oct. 2018. https://doi.org/10.1109/JSTSP.2018.2844818
  10. J. Scarlett, "Noisy Adaptive Group Testing: Bounds and Algorithms," IEEE Transactions on Information Theory, vol. 65, no. 6, pp. 3646-3661, Jun. 2019. https://doi.org/10.1109/TIT.2018.2883604