@inproceedings{e27b14f7ea1443a881870c76249e45fd,
title = "Hierarchical Fisher kernels for longitudinal data",
abstract = "We develop new techniques for time series classification based on hierarchical Bayesian generative models (called mixed-effect models) and the Fisher kernel derived from them. A key advantage of the new formulation is that one can compute the Fisher information matrix despite varying sequence lengths and varying sampling intervals. This avoids the commonly-used ad hoc replacement of the Fisher information matrix with the identity which destroys the geometric invariance of the kernel. Our construction retains the geometric invariance, resulting in a kernel that is properly invariant under change of coordinates in the model parameter space. Experiments on detecting cognitive decline show that classifiers based on the proposed kernel out-perform those based on generative models and other feature extraction routines, and on Fisher kernels that use the identity in place of the Fisher information.",
author = "Zhengdong Lu and Leen, {Todd K.} and Jeffrey Kaye",
year = "2009",
language = "English (US)",
isbn = "9781605609492",
series = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",
publisher = "Neural Information Processing Systems",
pages = "1961--1968",
booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",
note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",
}