Although many studies collect biomedical time series signals from multiple subjects, there is a dearth of models and methods for assessing the association between frequency domain properties of time series and other study outcomes. This article introduces the random Cramér representation as a joint model for collections of time series and static outcomes where power spectra are random functions...

In generalized canonical correlation analysis several sets of variables are analyzed simultaneously. This makes the method suited for the analysis of various types of data. For example, in marketing research, subjects may be asked to rate a set of objects on a set of attributes. For each individual, a data matrix can then be constructed where the objects are represented row-wise and the attribu...

Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair of linear projections onto a low dimensional space, where the correlation between two multidimensional variables is maximized after eliminating the influence of a third variable. Partial CCA is known to be closely related to a causality measure between two time series. However, partial CCA require...

Canonical correlation analysis (CCA) is a method for finding linear relations between two multidimensional random variables. This paper presents a generalization of the method to more than two variables. The approach is highly scalable, since it scales linearly with respect to the number of training examples and number of views (standard CCA implementations yield cubic complexity). The method i...

We present deep variational canonical correlation analysis (VCCA), a deep multiview learning model that extends the latent variable model interpretation of linear CCA (Bach and Jordan, 2005) to nonlinear observation models parameterized by deep neural networks (DNNs). Computing the marginal data likelihood, as well as inference of the latent variables, are intractable under this model. We deriv...

We propose a method for estimating face depth maps from color face images. The method is based on Canonical Correlation Analysis (CCA) which exploits the correlation between face color texture and surface depth. The results of experiments conducted on a database of 218 3D scans with corresponding color images show that only a small number of canonical factors are needed to describe the function...

BACKGROUND The perceived responsiveness of a healthcare system reflects its ability to satisfy reasonable expectations of the public with respect to non-medical services. Recently, there has been increasing attention paid to responsiveness in evaluating the performance of a healthcare system in a variety of service settings. However, the factors that affect the responsiveness have been inconclu...

BACKGROUND Infection of the CNS is considered to be the major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. Despite being identified worldwide as an important public health concern, studies on encephalitis are very few and often focus on particular types (with respect to causative agents) of encephalitis (e.g. West Nile, Japanese, etc.). M...