10,656 research outputs found
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data
We propose a novel approach for modeling multivariate longitudinal data in
the presence of unobserved heterogeneity for the analysis of the Health and
Retirement Study (HRS) data. Our proposal can be cast within the framework of
linear mixed models with discrete individual random intercepts; however,
differently from the standard formulation, the proposed Covariance Pattern
Mixture Model (CPMM) does not require the usual local independence assumption.
The model is thus able to simultaneously model the heterogeneity, the
association among the responses and the temporal dependence structure. We focus
on the investigation of temporal patterns related to the cognitive functioning
in retired American respondents. In particular, we aim to understand whether it
can be affected by some individual socio-economical characteristics and whether
it is possible to identify some homogenous groups of respondents that share a
similar cognitive profile. An accurate description of the detected groups
allows government policy interventions to be opportunely addressed. Results
identify three homogenous clusters of individuals with specific cognitive
functioning, consistent with the class conditional distribution of the
covariates. The flexibility of CPMM allows for a different contribution of each
regressor on the responses according to group membership. In so doing, the
identified groups receive a global and accurate phenomenological
characterization.Comment: Published at http://dx.doi.org/10.1214/15-AOAS816 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes well-suited
for modeling repulsion. In machine learning, the focus of DPP-based models has
been on diverse subset selection from a discrete and finite base set. This
discrete setting admits an efficient sampling algorithm based on the
eigendecomposition of the defining kernel matrix. Recently, there has been
growing interest in using DPPs defined on continuous spaces. While the
discrete-DPP sampler extends formally to the continuous case, computationally,
the steps required are not tractable in general. In this paper, we present two
efficient DPP sampling schemes that apply to a wide range of kernel functions:
one based on low rank approximations via Nystrom and random Fourier feature
techniques and another based on Gibbs sampling. We demonstrate the utility of
continuous DPPs in repulsive mixture modeling and synthesizing human poses
spanning activity spaces
2.5D multi-view gait recognition based on point cloud registration
This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM
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