In the presence of large dimensional datasets that contain many irrelevant features(variables), dimensionality reduction algorithms have proven to be useful in removing features with low variance and combine features with high correlation .
<coherence> In the presence of large dimensional datasets that contain many irrelevant features(variables), dimensionality reduction algorithms have proven to be useful in removing features with low variance and combine features with high correlation .
A central problem in machine learning and pattern recognition is the process of recognizing the most important features .