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The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality. [ABSTRACT FROM AUTHOR]

In this paper, we study the multi-task learning problem with a new perspective of considering the link structure of data and task relationship modeling simultaneously. In particular, we first introduce the Matrix Gaussian (MG) distribution and Matrix Generalized Inverse Gaussian (MGIG) distribution, then define a Matrix Gaussian Matrix Generalized Inverse Gaussian (MG-MGIG) prior. Based on this prior, we propose a novel multi-task learning algorithm, the Bayesian Multi-task Relationship Learning (BMTRL) algorithm. To incorporate the link structure into the framework of BMTRL, we propose link constraints between samples. Through combining the BMTRL algorithm with the link constraints, we propose the Bayesian Multi-task Relationship Learning with Link Constraints (BMTRL-LC) algorithm. Further, we apply the manifold theory to provide an extension of BMTRL-LC to data with no link structure. Specifically, BMTRL-LC is effective for multi-task learning with only limited training samples, which is not addressed in the existing literature. To make the computation tractable, we simultaneously use a convex optimization method and sampling techniques. In particular, we adopt two stochastic EM algorithms for BMTRL and BMTRL-LC, respectively. The experimental results on three real datasets demonstrate the promise of the proposed algorithms. [ABSTRACT FROM PUBLISHER]