Product partition principal components model for multiple change point detection in multivariate data

Add to your list(s) Download to your calendar using vCal

• Hayes, K (University of Limerick)
• Thursday 16 January 2014, 16:30-16:50
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact Mustapha Amrani.

This talk has been canceled/deleted

The product partition model (PPM) is a well known method for multiple change point analysis but only limited to univariate data. In this article, an extension of PPM called product partition principal component analysis model (PPPCM) is proposed, which employs dimensionality reduction approach to the problem of multiple change point in multivariate data. The PPPCM can be used to detect distributional changes in the mean and covariance of a multivariate Gaussian data, providing a smaller dimensional representation of the data. The utility of the proposed PPPCM is demonstrated through experiments on simulated and real datasets. We also develop the model to address change-point analysis in “long” time series.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.