Semi-supervised incremental feature extraction algorithm forrnlarge-scale data stream

摘要

In big data era, how to process large-scale data stream is one of the existing challenges. Feature extractionrnmethod has attracted much attention because of its effectiveness to data classification. Traditional classificationrnalgorithms may take less advantage of labeled samples information. Online learning and out-of-samplernproblems are also hot topics recently. To solve these problems, a novel algorithm named semi-supervisedrnincremental feature extraction algorithm is proposed in this paper. First, we extract feature incrementallyrnin unsupervised way. Then we propose a semi-supervised subspace learning algorithm by taking advantagernof class information to adjust k-nearest neighbor weights. Third, we combine the unsupervised andrnsemi-supervised feature extraction approaches to obtain objective function, in order to solve the out-ofsamplernlearning problem. Experiments have been carried out on Machine learning datasets of Universityrnof California Irvine (UCI) datasets and real-world face image datasets (Olivetti faces (ORL), Yale, YaleB,rnand Rendered face). To demonstrate the proposed algorithm’s expandability to process the large-scale datarnstream, classification experiments using Spark skill in parallel computation environment are performed,rnwith comparisons with some related semi-supervised feature extraction methods. The experiment results andrncomputational complex comparison demonstrate that the proposed algorithm can obtain good performance.rnCopyright © 2016 John Wiley & Sons, Ltd.