基于张量模式的降维方法研究

Survey of the Dimensionality Reduction based on Tensor Pattern

经典的向量子空间是以数据流行的向量形式表示的,而在现实应用中很多是以张量模式存在的,从而提出了张量子空间.张量模式是向量模式的扩展和推广,已经广泛的应用到模式识别和数据降维等领域.主要描述了张量的定义和基本运算,对张量子空间,张量逼近和张量脸进行了具体的分析,通过张量特有的分解方法得到最优解从而达到降维的目的,本文最后提出张量以后有待发展的方向.

Classical vector subspace learning algorithms work with vectorized representations of data manifold,while in reality,many of the tensor model is put forward,thus,come up with a novel tensor subspace learning algorithm.Tensor model is a vector model and promotion,has been widely applied to pattern recognition and data dimension reduction etc.This paper describes the definition and basic tensor of quantum computing,tensor subspace,tensor approximation and tensorface a concrete analysis,through the tensor unique decomposition method for optimal solution to achieve the goal,the dimension reduction in after the tensor to the direction of development.