One-Pass Learning with Incremental and Decremental Features
This addresses a crucial but rarely studied problem for applications like environment monitoring and mobile game recommendation, where features change dynamically, though it appears incremental in method.
The paper tackles the problem of learning with evolving features in streaming data, where features can vanish or be augmented over time, and presents the OPID approach that compresses information from vanished features and expands to include new ones, achieving one-pass learning without storing the entire dataset.
In many real tasks the features are evolving, with some features being vanished and some other features augmented. For example, in environment monitoring some sensors expired whereas some new ones deployed; in mobile game recommendation some games dropped whereas some new ones added. Learning with such incremental and decremental features is crucial but rarely studied, particularly when the data coming like a stream and thus it is infeasible to keep the whole data for optimization. In this paper, we study this challenging problem and present the OPID approach. Our approach attempts to compress important information of vanished features into functions of survived features, and then expand to include the augmented features. It is the one-pass learning approach, which only needs to scan each instance once and does not need to store the whole data, and thus satisfy the evolving streaming data nature. The effectiveness of our approach is validated theoretically and empirically.