방법 비교

	Isomap	주성분 분석
분야	머신러닝	머신러닝
계열	Latent structure	Machine learning
기원 연도	2000	2002
창시자	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
유형	Manifold learning / nonlinear dimensionality reduction	Unsupervised dimensionality reduction
원전	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
별칭	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
관련	3	3
요약	Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.