DOI: 10.1007/978-3-540-87881-0_7
|View full text |Cite
|
Sign up to set email alerts
|

A Sparse Regression Mixture Model for Clustering Time-Series

Abstract: Abstract. In this study we present a new sparse polynomial regression mixture model for fitting time series. The contribution of this work is the introduction of a smoothing prior over component regression coefficients through a Bayesian framework. This is done by using an appropriate Student-t distribution. The advantages of the sparsity-favouring prior is to make model more robust, less independent on order p of polynomials and improve the clustering procedure. The whole framework is converted into a maximum… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
references
References 14 publications
0
0
0
Order By: Relevance