Description
Title: A CRITERIA FOR DETECTING INTRINSIC DIMENSIONALITY BASED ON LOCALLY LINEAR EMBEDDING
Abstract: The Locally Linear Embedding (LLE) algorithm, which is frequently used in dimensionality reduction, is reviewed in this work. With a focus on the correspondences between the closest neighbors in the original and embedded spaces, we note that LLE does not change into a neighborhood-preserving algorithm when low-dimensional embedding spaces are specified. In order to determine the minimal intrinsic dimensionality necessary for neighborhood preservation, we thus propose the “neighborhood-preserving ratio” criterion. On synthetic data sets like S-curve, Swiss roll, and a dataset of grayscale images, we test its efficacy.
Keywords: LLE, dimensionality reduction, intrinsic dimensionality, neighborhood preserving
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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