Abstract:Increase in dimensions of the data decrease the performance of the machine learning systems as the increase in the dimensions increase the problem space under analysis make data sparse. As the efficiency of the machine learning algorithms directly relates to the volume of the test data, increased space demands more data for better learning opportunities. To address this challenge, most of the time we tend to reduce some of the data dimensions searching for dimensions which are not directly related to the problem under analysis. For efficient reduction of dimensions we need to address the question "what is the idea dimensionality we can address without compromising on the sensitive of the dimensions?" This paper outline the problem of dimensionality not just from the angle of issues with high dimensional data leading to the reduction of dimensions but analyses how to efficiency balance the dimensions through better data projection techniques for more accurate results.
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