Abstract
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.
Highlights
With the advancement of information technology over the last decade, digital resources have penetrated into all fields in our society, generating big data, which present a new challenge to data mining and information retrieval [1]
If the features output by dimensionality reduction (DR) can well preserve their pairwise distances in original space, DR suppresses the loss of training accuracy; we evaluate the effects of structural random matrices (SRMs) on pairwise distances between text features
We develop a compressive sensing- (CS-)based model for text classification tasks
Summary
With the advancement of information technology over the last decade, digital resources have penetrated into all fields in our society, generating big data, which present a new challenge to data mining and information retrieval [1]. In ML, many classifiers can be used to classify texts, such as support vector machine (SVM) [10], decision tree [11], adaptive boosting (AdaBoost) [12], K-nearest neighbor (KNN) [13], and Naıve Bayes [14] To train these classifiers, texts must be represented as feature vectors by some feature extraction models, among which the commonest is Bag of Words (BOW) [15]. By being embedded into the neural network, the autoencoder can end up learning a low-dimensional representation very similar to PCAs. Compared with the above-mentioned DR techniques, random projection [23, 24] is a better choice, since it avoids the model training, but it is still a challenge to store random projections due to the huge dimensionality of text feature.
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