Vertaile menetelmiä

	Pysyvä homologia	Spektrinen klusterointi
Tieteenala	Topologia	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2002	2002
Kehittäjä	Edelsbrunner, Letscher & Zomorodian	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tyyppi	Topological feature extraction algorithm	Graph-based clustering (spectral method)
Alkuperäislähde	Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2002). Topological persistence and simplification. Discrete & Computational Geometry, 28(4), 511–533.	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Rinnakkaisnimet	Topological Persistence, Persistence Barcodes, Persistent Betti Numbers, Kalıcı Homoloji	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Liittyvät	2	5
Tiivistelmä	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.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.