支持张量机算法优化研究综述

Research review of algorithm optimization of support tensor machine

随着大数据时代的发展,张量数据被广泛应用于各个领域,支持张量机算法作为张量数据分类最有效的方法之一,成为了研究者广泛讨论的课题。本文简单介绍了支持张量机模型对张量数据处理的优势,并详细介绍了如何从减少储存空间、缩短分类时间、提高模型精度和扩展模型性能等方面对支持张量机模型进行优化,同时介绍了支持张量机在现实领域中的应用,最后从支持张量机算法优化方面提出了展望。

With the development of the era of big data,tensor data has been widely applied in various fields. As one of the most effective methods for the classification of tensor data,supporting tensor algorithm has also become a subject of close attention.Meanwhile,supporting tensor algorithm and its optimization have been widely discussed by researchers. This article simply introduces the support tensor machine model and support the superiority of the tensor data processing of the tensor machine basic mathematical model,and introduces in detail how to reduce the storage space,shortening the time of classification,improve the model accuracy and extension model performance to optimize the model and the application in the field of reality,and finally put forward from the aspects of support tensor machine algorithm to optimize the future.