An Iterative Locally Linear Embedding Algorithm

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.