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• W2891882345 abstract "Dimensionality Reduction is one of the preferred techniques for addressing the problem of the curse of dimensionality associated with high-dimensional data. At present, various significant research works have been already carried out toward emphasizing the dimensional reduction methods with respect to projection-based, statistical-based, and dictionary-based. However, it is still an open question to explore the best technique of dimensional reduction. Hence, we present a compact summary of our investigation towards finding the contribution of existing research methods of dimensional reduction. The paper outlines most frequently adopted techniques of dimensional reduction. At the same time, this survey also emphasizes on exploring the problems addressed by the present researchers with an aid of their own techniques associated with both advantages, limitations, and addressing the issues of the curse of dimensionality. The survey also introduces the latest research progress and significant research gap associated with the existing literature." @default.
• W2891882345 created "2018-09-27" @default.
• W2891882345 creator A5017648365 @default.
• W2891882345 creator A5024813120 @default.
• W2891882345 date "2018-09-18" @default.
• W2891882345 modified "2023-09-25" @default.
• W2891882345 title "Review of Existing Research Contribution Toward Dimensional Reduction Methods in High-Dimensional Data" @default.
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• W2891882345 doi "https://doi.org/10.1007/978-981-10-8681-6_37" @default.
• W2891882345 hasPublicationYear "2018" @default.
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