A constrained matrix-variate Gaussian process for transposable data
نویسندگان
چکیده
منابع مشابه
The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking
We propose a novel hierarchical model for multitask bipartite ranking. The proposed approach combines a matrix-variate Gaussian process with a generative model for task-wise bipartite ranking. In addition, we employ a novel trace constrained variational inference approach to impose low rank structure on the posterior matrix-variate Gaussian process. The resulting posterior covariance function i...
متن کاملSparse matrix-variate Gaussian process blockmodels for network modeling
We face network data from various sources, such as protein interactions and online social networks. The network data often comprise pairwise measurements, e.g., presence or absence of links between pairs of objects. Given the data, a critical problem is to model network interactions and identify latent groups of network nodes. This problem is challenging due to many reasons. For example, the ne...
متن کاملMatrix-Variate Dirichlet Process Priors with Applications
In this paper we propose a matrix-variate Dirichlet process (MATDP) for modeling the joint prior of a set of random matrices. Our approach is able to share statistical strength among regression coe cient matrices due to the clustering property of the Dirichlet process. Moreover, since the base probability measure is de ned as a matrix-variate distribution, the dependence among the elements of e...
متن کاملA Tensor-Variate Gaussian Process for Classification of Multidimensional Structured Data
As tensors provide a natural and efficient representation of multidimensional structured data, in this paper, we consider probabilistic multinomial probit classification for tensor-variate inputs with Gaussian processes (GP) priors placed over the latent function. In order to take into account the underlying multimodes structure information within the model, we propose a framework of probabilis...
متن کاملMatrix-Variate Dirichlet Process Mixture Models
We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In this paper we develop a new Bayesian nonparametric model for such a setting based on Dirichlet process priors. Building on an additive kernel model, we allow each sample to have its own regression matrix. Although this o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2014
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-014-5444-1