Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss.

中文翻译：

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加快文本分类的压缩感知模型。

通过自动对大量文本文档进行分类，文本分类在大数据的各种应用中起着重要作用。但是，文本特征的高维和稀疏性对有效分类提出了挑战。在本文中，我们提出了一种基于压缩感知（CS-）的模型来加快文本分类。使用CS来减少特征空间的大小，我们的模型在训练文本分类器时具有较低的时间和空间复杂度，并且CS的受限制的等距特性（RIP）确保在文本处理过程中可以很好地保留文本特征之间的成对距离降维。特别是，通过结构随机矩阵（SRM），CS在随机投影的构造中不受计算和存储限制。