Dynamic Structure Embedded Online Multiple-Output Regression for Streaming Data.
IEEE Trans. Pattern Anal. Mach. Intell.(2019)
摘要
Online multiple-output regression is an important machine learning technique for modeling, predicting, and compressing multi-dimensional correlated data streams. In this paper, we propose a novel online multiple-output regression method, called MORES, for streaming data. MORES can dynamically learn the structure of the regression coefficients to facilitate the model's continuous refinement. Considering that limited expressive ability of regression models often leading to residual errors being dependent, MORES intends to dynamically learn and leverage the structure of the residual errors to improve the prediction accuracy. Moreover, we introduce three modified covariance matrices to extract necessary information from all the seen data for training, and set different weights on samples so as to track the data streams' evolving characteristics. Furthermore, an efficient algorithm is designed to optimize the proposed objective function, and an efficient online eigenvalue decomposition algorithm is developed for the modified covariance matrix. Finally, we analyze the convergence of MORES in certain ideal condition. Experiments on two synthetic datasets and three real-world datasets validate the effectiveness and efficiency of MORES. In addition, MORES can process at least 2,000 instances per second (including training and testing) on the three real-world datasets, more than 12 times faster than the state-of-the-art online learning algorithm.
更多查看译文
关键词
Data models,Linear programming,Correlation,Current measurement,Prediction algorithms,Load modeling
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络