Enhancing incremental learning with one-class envelopes for online unlabeled mixed samples

Incremental learning is crucial for handling classification tasks involving continuously increasing new categories. Traditional models often grapple with challenges such as data forgetting and high storage requirements. An approach grounded in one-class envelope techniques effectively mitigates these concerns. However, this method introduces two new challenges: swiftly determining the class label of a new sample, and effectively distinguishing mixed samples online before training new sub-classifiers. To address these issues, this paper introduces an efficient incremental learning strategy that leverages one-class envelopes, where a dedicated one-class classifier is built for each category. A novel hybrid-distance-based search algorithm for the target category is introduced to enhance prediction efficiency during the testing phase. Moreover, for online mixed samples from multiple unknown categories, the K-means algorithm is employed to cluster all outlier samples. Silhouette coefficients are then calculated to identify well-defined clusters for subsequent classifier training. Experimental results show that the proposed approach reduce prediction time by 90% on Cifar100-30 dataset, and the incremental process for unlabeled mixed samples on the MNIST dataset achieves up to 55.3% performance improvement.