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Effective Means of Handling Curse of Dimensionality


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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Distinguished Architects  Randy Arthur (Americas) serves as product owner for CSC’s IaaS offerings and as a lead solutions architect for complex integration projects involving cloud computing technologies. During his 16-year career with CSC, Randy  has worked successfully in various roles including midrange service delivery, pre-sales solution development and product management. He was the first CTO of CSC’s Cloud  technology “incubator.” Bio on | LinkedIn| Twitter
Graham Chastney(UKI&N) is a global domain architect experienced in workplace technologies,  solution strategy and solution governance. He is a global collaborator who is relied upon to provide thought leadership to solution teams and to build and development teams. Graham is the founder  and lead author of the Technology Perspectives blog, which he regards as part of a broader ambition to …