基于正交分解的文本分类模型

针对文本分类领域中向量空间模型维数过高和空间扭曲的问题，提出一种基于正交分解的新模型。借用物理学中力的正交分解，将高维的文本向量映射到低维的以类别为坐标轴的空间中，解决了高维的向量和扭曲的空间这2个问题。实验表明，与向量空间模型相比，新模型下分类速度有较大提高，精度也有所增加。

Text Classification Model Based on Orthogonal Decomposition

In text classification area,Vector Space Model(VSM) is the most widely used model while it has two drawbacks:high dimensions and warped space.This paper presents a new model based on orthogonal decomposition.In this model,higher dimensional vectors of texts are mapped in a lower dimensional space which uses categories as its coordinate axes to solve these two drawbacks.Experiment shows that under the new model,the classification process is speeded up to a considerable degree and the precision is increased.