Advanced

Estimating Periodicities in Symbolic Sequences Using Sparse Modeling

Adalbjörnsson, Stefan Ingi LU ; Swärd, Johan LU ; Wallin, Jonas LU and Jakobsson, Andreas LU (2015) In IEEE Transactions on Signal Processing 63(8). p.2142-2150
Abstract
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,... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
DNA, Data analysis, Periodicity, Spectral estimation, Symbolic sequences, Indexes, Logistics, Maximum likelihood estimation
in
IEEE Transactions on Signal Processing
volume
63
issue
8
pages
2142 - 2150
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000351467700020
  • scopus:84926029966
ISSN
1053-587X
DOI
10.1109/TSP.2015.2404314
language
English
LU publication?
yes
id
d5bba0c0-f15d-4ab4-b39f-a7653a030150 (old id 5159354)
date added to LUP
2015-05-29 12:49:43
date last changed
2017-03-16 09:33:05
@article{d5bba0c0-f15d-4ab4-b39f-a7653a030150,
  abstract     = {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.},
  author       = {Adalbjörnsson, Stefan Ingi and Swärd, Johan and Wallin, Jonas and Jakobsson, Andreas},
  issn         = {1053-587X},
  keyword      = {DNA,Data analysis,Periodicity,Spectral estimation,Symbolic sequences,Indexes,Logistics,Maximum likelihood estimation},
  language     = {eng},
  number       = {8},
  pages        = {2142--2150},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Signal Processing},
  title        = {Estimating Periodicities in Symbolic Sequences Using Sparse Modeling},
  url          = {http://dx.doi.org/10.1109/TSP.2015.2404314},
  volume       = {63},
  year         = {2015},
}