Tensor decompositions and sparse log-linear models
نویسندگان
چکیده
منابع مشابه
Tensor Decompositions and Sparse Log-linear Models.
Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these noti...
متن کاملParCube: Sparse Parallelizable Tensor Decompositions
How can we efficiently decompose a tensor into sparse factors, when the data does not fit in memory? Tensor decompositions have gained a steadily increasing popularity in data mining applications, however the current state-of-art decomposition algorithms operate on main memory and do not scale to truly large datasets. In this work, we propose ParCube, a new and highly parallelizable method for ...
متن کاملRobust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملSparse Nested Markov models with Log-linear Parameters
Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which captures equality constraints found in marginals of directed acyclic graph (D...
متن کاملDecomposable Log-linear Models Decomposable Log-linear Models
The present paper considers discrete probability models with exact computational properties. In relation to contingency tables this means closed form expressions of the maksimum likelihood estimate and its distribution. The model class includes what is known as decomposable graphical models, which can be characterized by a structured set of conditional independencies between some variables give...
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: The Annals of Statistics
سال: 2017
ISSN: 0090-5364
DOI: 10.1214/15-aos1414