Dimensionality reduction in text classification using scatter method

2014 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Jyri Saarikoski ◽  
Jorma Laurikkala ◽  
Kalervo Järvelin ◽  
Markku Siermala ◽  
Martti Juhola
Keyword(s):  
Dimensionality Reduction ◽  
Text Classification ◽  
Scatter Method

scholarly journals A method of dimensionality reduction by selection of components in principal component analysis for text classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1499-1506 ◽  
Author(s):  
Yangwu Zhang ◽  
Guohe Li ◽  
Heng Zong
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Text Classification ◽  
Principal Components ◽  
Principal Component ◽  
Method Of Dimensionality Reduction ◽  
Components Analysis ◽  
Selection Of

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. Jolliffe, 2002). In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

Text Categorisation Through Dimensionality Reduction Using Wavelet Transform

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
Jorge Chamorro-Padial ◽  
Rosa Rodríguez-Sánchez
Keyword(s):  
Wavelet Transform ◽  
Dimensionality Reduction ◽  
Text Classification ◽  
High Energy ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Vertical Orientation ◽  
Method Of Dimensionality Reduction ◽  
Horizontal Orientation ◽  
Relevant Level

This paper proposes a new method of dimensionality reduction when performing Text Classification, by applying the discrete wavelet transform to the document-term frequencies matrix. We analyse the features provided by the wavelet coefficients from the different orientations: (1) The high energy coefficients in the horizontal orientation correspond to relevant terms in a single document. (2) The high energy coefficients in the vertical orientation correspond to relevant terms for a single document, but not for the others. (3) The high energy coefficients in the diagonal orientation correspond to relevant terms in a document in comparison to other terms. If we filter using the wavelet coefficients and fulfil these three conditions simultaneously, we can obtain a reduced vocabulary of the corpus, with less dimensions than in the original one. To test the success of the reduced vocabulary, we recoded the corpus with the new reduced vocabulary and we obtained a statistically relevant level of accuracy for document classification.

Abstracting for Dimensionality Reduction in Text Classification

2012 ◽  
Vol 28 (2) ◽  
pp. 115-138 ◽  
Author(s):  
Richard A. McAllister ◽  
Rafal A. Angryk
Keyword(s):  
Dimensionality Reduction ◽  
Text Classification
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