Compressed Smooth Sparse Decomposition

Published Online:https://doi.org/10.1287/ijds.2022.0023

References

  • Augusto CRA, Fauth AC, Navia CE, Shigeouka H, Tsui KH (2011) Connection among spacecrafts and ground level observations of small solar transient events. Experiment. Astronomy 31(2):177–197.Google Scholar
  • Baraniuk R, Davenport M, DeVore R, Wakin M (2008) A simple proof of the restricted isometry property for random matrices. Constructive Approximation 28(3):253–263.Google Scholar
  • Bouwmans T, Zahzah EH (2014) Robust PCA via principal component pursuit: A review for a comparative evaluation in video surveillance. Comput. Vision Image Understanding 122:22–34.Google Scholar
  • Candes EJ (2008) The restricted isometry property and its implications for compressed sensing. Competus Rendus Math. 346(9–10):589–592.Google Scholar
  • Candes EJ, Tao T (2005) Decoding by linear programming. IEEE Trans. Inform. Theory 51(12):4203–4215.Google Scholar
  • Candes EJ, Romberg JK, Tao T (2006) Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59(8):1207–1223.Google Scholar
  • Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J. ACM 58(3):1–37.Google Scholar
  • De Boor C, De Boor C (1978) A Practical Guide to Splines, vol. 27 (Springer-Verlag, New York).Google Scholar
  • Duarte MF, Baraniuk RG (2011) Kronecker compressive sensing. IEEE Trans. Image Processing 21(2):494–504.Google Scholar
  • Eilers PH, Marx BD (1996) Flexible smoothing with B-splines and penalties. Statist. Sci. 11(2):89–121.Google Scholar
  • Giannakis GB, Mateos G, Farahmand S, Kekatos V, Zhu H (2011) USPACOR: Universal sparsity-controlling outlier rejection. Tichavsky P, Cernocky H, Prochazka A, eds. 2011 IEEE Internat. Conf. Acoustics Speech Signal Processing (IEEE, New York), 1952–1955.Google Scholar
  • Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev. 51(3):455–500.Google Scholar
  • Mardani M, Mateos G, Giannakis GB (2013) Recovery of low-rank plus compressed sparse matrices with application to unveiling traffic anomalies. IEEE Trans. Inform. Theory 59(8):5186–5205.Google Scholar
  • Marques EC, Maciel N, Naviner L, Cai H, Yang J (2018) A review of sparse recovery algorithms. IEEE Access 7:1300–1322.Google Scholar
  • Mateos G, Giannakis GB (2011) Robust nonparametric regression by controlling sparsity. 2011 IEEE Internat. Conf. Acoustics Speech Signal Processing (ICASSP).Google Scholar
  • Minaee S, Abdolrashidi A, Wang Y (2015) Screen content image segmentation using sparse-smooth decomposition. 2015 49th Asilomar Conf. Signals Systems Comput.Google Scholar
  • Mou S, Wang A, Zhang C, Shi J (2021) Additive tensor decomposition considering structural data information. IEEE Trans. Automation Sci. Engrg. 19(4):2904–2917.Google Scholar
  • Rani M, Dhok SB, Deshmukh RB (2018) A systematic review of compressive sensing: Concepts, implementations and applications. IEEE Access 6:4875–4894.Google Scholar
  • Recht B, Fazel M, Parrilo PA (2010) Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev. 52(3):471–501.Google Scholar
  • Tanner J, Vary S (2020) Compressed sensing of low-rank plus sparse matrices. Preprint, submitted July 18, https://arxiv.org/abs/2007.09457v1.Google Scholar
  • Unser M (1999) Splines: A perfect fit for signal and image processing. IEEE Signal Processing Magazine 16(6):22–38.Google Scholar
  • Vershynin R (2018) High-Dimensional Probability: An Introduction with Applications in Data Science, vol. 47 (Cambridge University Press, Cambridge, United Kingdom).Google Scholar
  • Wang A, Xian X, Tsung F, Liu K (2018) A spatial-adaptive sampling procedure for online monitoring of big data streams. J. Quality Tech. 50(4):329–343.Google Scholar
  • Waters AE, Sankaranarayanan AC, Baraniuk RG (2011) SpaRCS: Recovering low-rank and sparse matrices from compressive measurements. Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ, eds. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 1089–1097.Google Scholar
  • Xu H, Caramanis C, Sanghavi S (2012) Robust PCA via outlier pursuit. IEEE Trans. Inform. Theory 58(5):3047–3064.Google Scholar
  • Yan H, Paynabar K, Shi J (2017) Anomaly detection in images with smooth background via smooth-sparse decomposition. Technometrics 59(1):102–114.Google Scholar
  • Yan H, Paynabar K, Shi J (2018) Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition. Technometrics 60(2):181–197.Google Scholar
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