Linganisha mbinu

	Ufumbuzi wa Kina wa Kienyeji (LLE)	Algorithmu ya Ramani (Mapper Algorithm)
Nyanja	Ujifunzaji wa Mashine	Topolojia
Familia	Machine learning	Machine learning
Mwaka wa asili	2000	2007
Mwanzilishi	Sam Roweis & Lawrence Saul	Singh, Mémoli & Carlsson
Aina	Nonlinear manifold dimensionality reduction	Graph-based topological summarization
Chanzo asilia	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.	Singh, G., Mémoli, F., & Carlsson, G. (2007). Topological methods for the analysis of high dimensional data sets and 3D object recognition. Eurographics Symposium on Point-Based Graphics, 91–100.
Majina mbadala	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme	Topological Mapper, TDA Mapper, Reeb Graph Approximation, Eşleyici Algoritma
Zinazohusiana	3	2
Muhtasari	Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map.	The Mapper algorithm is a method in topological data analysis (TDA) that produces a graph-based summary of the shape of high-dimensional point cloud data. Introduced by Singh, Mémoli, and Carlsson in 2007 at the Eurographics Symposium on Point-Based Graphics, Mapper constructs a simplicial complex — typically a graph — that captures the global topological and geometric structure of a dataset without requiring a fixed embedding or metric assumption.