Climate Prediction via Matrix Completion
نویسندگان
چکیده
Recently, machine learning has been applied to the problem of predicting future climates, informed by the multi-model ensemble of physics-based climate models that inform the Intergovernmental Panel on Climate Change (IPCC). Past work (Monteleoni et al., 2011, McQuade and Monteleoni, 2012) demonstrated the promise of online learning algorithms applied to this problem. Here we propose a novel approach, using sparse matrix completion.
منابع مشابه
Protein-Protein Interaction Prediction via Structured Matrix Completion
This paper considers how to computationally predict unknown protein-protein interactions (PPIs) given the experimentally verified PPIs. Matrix completion, a very popular machine learning technique that can be used to to infer the missing part of a matrix, has been introduced to recover the missing interactions of an incomplete PPI network. The benefit of Matrix completion is that it does not re...
متن کاملSocial Trust Prediction via Max-norm Constrained 1-bit Matrix Completion
Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completion framework, with each entry indicating the trustness or distrustness. However, there are two challenges for the social trust problem: 1) the observed data are with sign (1-bit) measurements; 2) they are typically sa...
متن کاملGraph Matrix Completion in Presence of Outliers
Matrix completion problem has gathered a lot of attention in recent years. In the matrix completion problem, the goal is to recover a low-rank matrix from a subset of its entries. The graph matrix completion was introduced based on the fact that the relation between rows (or columns) of a matrix can be modeled as a graph structure. The graph matrix completion problem is formulated by adding the...
متن کاملLog-Normal Matrix Completion for Large Scale Link Prediction
The ubiquitous proliferation of online social networks has led to the widescale emergence of relational graphs expressing unique patterns in link formation and descriptive user node features. Matrix Factorization and Completion have become popular methods for Link Prediction due to the low rank nature of mutual node friendship information, and the availability of parallel computer architectures...
متن کاملSemi-Supervised Matrix Completion for Cross-Lingual Text Classification
Cross-lingual text classification is the task of assigning labels to observed documents in a label-scarce target language domain by using a prediction model trained with labeled documents from a label-rich source language domain. Cross-lingual text classification is popularly studied in natural language processing area to reduce the expensive manual annotation effort required in the target lang...
متن کامل