Estimating Periodicities in Symbolic Sequences Using Sparse Modeling

Summary, in English

In this paper, we propose a method for estimating statistical periodicities in symbolic sequences. Different from other common approaches used for the estimation of periodicities of sequences of arbitrary, finite, symbol sets, that often map the symbolic sequence to a numerical representation, we here exploit a likelihood-based formulation in a sparse modeling framework to represent the periodic behavior of the sequence. The resulting criterion includes a restriction on the cardinality of the solution; two approximate solutions are suggested—one greedy and one using an iterative convex relaxation strategy to ease the cardinality restriction. The performance of the proposed methods are illustrated using both simulated and real DNA data, showing a notable performance gain as compared to other common estimators.

• Författare: Stefan Ingi Adalbjörnsson; Johan Swärd; Jonas Wallin; Andreas Jakobsson
• Avdelning/ar: Matematisk statistik; Statistical Signal Processing Group; Biomedical Modelling and Computation
• Publiceringsår: 2015
• Språk: Engelska
• Sidor: 2142-2150
• Publikation/Tidskrift/Serie: IEEE Transactions on Signal Processing
• Volym: 63
• Issue: 8
• Fulltext: - 13 MB; Download statistics
• Länkar: http://dx.doi.org/10.1109/TSP.2015.2404314
• Dokumenttyp: Artikel i tidskrift
• Förlag: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Ämne: Probability Theory and Statistics; Mathematics; Computer Vision and Robotics (Autonomous Systems)
• Nyckelord: DNA; Data analysis; Periodicity; Spectral estimation; Symbolic sequences; Indexes; Logistics; Maximum likelihood estimation
• Status: Published
• Forskningsgrupp: Statistical Signal Processing Group; Biomedical Modelling and Computation
• ISBN/ISSN/Övrigt: ISSN: 1053-587X