Research Area: Machine Learning
Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.
Keywords:
dimensionality reduction
preprocessing
redundant data
learning accuracy
result comprehensibility
pattern recognition
Author(s) Name: Samina Khalid; Tehmina Khalil; Shamila Nasreen
Journal name:
Conferrence name: 2014 Science and Information Conference
Publisher name: IEEE
DOI: 10.1109/SAI.2014.6918213
Volume Information:
