Salīdzināt metodes

	Pastīgā homologija	Lokāli lineārā iegulšana (LLE)
Nozare	Topoloģija	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2002	2000
Autors	Edelsbrunner, Letscher & Zomorodian	Sam Roweis & Lawrence Saul
Tips	Topological feature extraction algorithm	Nonlinear manifold dimensionality reduction
Pirmavots	Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2002). Topological persistence and simplification. Discrete & Computational Geometry, 28(4), 511–533.	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
Citi nosaukumi	Topological Persistence, Persistence Barcodes, Persistent Betti Numbers, Kalıcı Homoloji	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
Saistītās	2	3
Kopsavilkums	Persistent homology is a method in topological data analysis that quantifies the multi-scale topological structure of data by tracking connected components, loops, and voids as a scale parameter varies. Introduced by Edelsbrunner, Letscher, and Zomorodian in 2002, it encodes topological features through their birth and death scales, producing persistence diagrams or barcodes that serve as compact, coordinate-free descriptors of shape. The approach is robust to noise and provides a mathematically rigorous bridge between discrete data and algebraic topology.	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.