An Iterative Locally Linear Embedding Algorithm
Deguang Kong (The University of Texas at Arlington), Chris H.Q. Ding, (The University of Texas at Arlington), Heng Huang (The University of Texas, at Arlington), Feiping Nie (The University of Texas at Arlington)

TL;DR
This paper introduces an improved iterative locally linear embedding algorithm that enhances dimension reduction, classification, and clustering by combining nonnegative constraints, iterative refinement, and relaxed similarity constraints.
Contribution
It presents a novel iterative LLE method that integrates nonnegative constraints, iterative steps, and sparse similarity learning, improving upon traditional LLE techniques.
Findings
Significant improvements in classification accuracy.
Enhanced clustering performance.
Effective dimension reduction with better data structure preservation.
Abstract
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedding. We further propose to iterate the two steps in LLE repeatedly to improve the results. Thirdly, we relax the kNN constraint of LLE and present a sparse similarity learning algorithm. The final Iterative LLE combines these three improvements. Extensive experiment results show that iterative LLE algorithm significantly improve both classification and clustering results.
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Taxonomy
TopicsFace and Expression Recognition · Advanced Computing and Algorithms · Blind Source Separation Techniques
