半监督多视图学习在大数据分析中的应用探讨

[摘要] 半监督多视图学习是机器学习领域一种极具潜力的大数据处理和分析方法，该方法能有效处理异构和半监督数据，并能方便地在线化和并行化，适合处理海量数据。该方法在大数据时代的应用前景值得研究人员和业界关注。指出未来需要通过引入其他领域新的研究技术和成果，不断丰富和完善半监督多视图学习的理论体系和算法设计，并在实验和实践中不断检验和探索。

[关键词] 半监督；多视图；大数据；并行化

[Abstract] This paper introduces a promising machine-learning paradigm called semi-supervised multi-view learning. With this paradigm, information is extracted from heterogeneous and semi-supervised data sets. Lately, multi-view learning has been scaled up online and through parallelization to deal with emerging big data challenges. Due to its successful application in many research domains and the fact that it has been explored and used by leading companies, multi-view learning may have a future in the big-data era as a major data analytic technique. New research techniques should be introduced into this area to improve the theoretical system and algorithm design of semi-supervised multi-view learning.

[Keywords] semi-supervised; multi-view; big data; parallelization