Abstract-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. We observe that limited expressive ability of the regression model, especially in the preliminary stage of online update, often leads to the variables in the residual errors being dependent. In light of this point, MORES intends to dynamically learn and leverage the structure of the residual errors to improve the prediction accuracy. Moreover, we define three statistical variables to exactly represent all the seen samples for incrementally calculating prediction loss in each online update round, which can avoid loading all the training data into memory for updating model, and also effectively prevent drastic fluctuation of the model in the presence of noise. Furthermore, we introduce a forgetting factor to set different weights on samples so as to track the data streams' evolving characteristics quickly from the latest samples. Experiments on three real-world datasets validate the effectiveness and efficiency of the proposed method.
MORES is another algorithm for multiple-output regression which dynamically learns the structure of the regression coefficients REF .
MORES: Online Incremental Multiple-Output Regression for Data Streams
ArXiv
ArXiv
Computer Science