A Survey of Dimensionality Reduction methods for multidimensional data

A Survey of Dimensionality Reduction methods for multidimensional data
Author(s):
Pritika Mehra, Mini Singh Ahuja
Keywords:
Dimensionality Reduction, High Dimensional data, principle component analysis, autoencoders
Abstract
Multidimensional data is more prevalent due to the rapid expansion of computational biometric and e-commerce applications. As a result, mining multidimensional data is a crucial issue with significant practical implications. The curse of dimensionality and, more importantly, the meaning of the similarity measure in the high dimension space are two specific difficulties that arise while mining high-dimensional data. The challenges and methods for dimensionality reduction of multidimensional data are surveyed in this work.
Article Details
Unique Paper ID: 157296 Publication Volume & Issue: Volume 9, Issue 6 Page(s): 567 - 571
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