Adaptive Matrix Sketching and Clustering for Semisupervised Incremental Learning

Abstract

Semisupervised incremental learning is the task of classifying data streams with partially labeled data when annotation information is difficult to obtain. Besides the sequential learning manner and lack of label information, multiple novel classes and concept drift may emerge from incremental learning. Most previous studies have only considered these problems in part. To tackle challenges involved in semisupervised incremental learning, an adaptive matrix sketching and clustering method is proposed in this letter, which cohesively and adaptively classifies known classes, identifies multiple novel classes, and updates the learning model. Experiments were conducted to evaluate this method on three benchmark datasets, containing various data types, including network attack analysis, the geospatial information of forests, and images of handwritten numbers. Results validated the effectiveness of our proposed method and its superiority over many previous studies.