विधियों की तुलना करें

	कर्नेल पीसीए	ऑटोएन्कोडर	सपोर्ट वेक्टर मशीन (वर्गीकरण)
क्षेत्र	मशीन अधिगम	गहन अधिगम	मशीन अधिगम
परिवार	Latent structure	Machine learning	Machine learning
उद्भव वर्ष	1998	2006	1995
प्रवर्तक	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Hinton, G.E. & Salakhutdinov, R.R.	Cortes, C. & Vapnik, V.
प्रकार	Nonlinear dimensionality reduction via kernel trick	Neural network (encoder-decoder)	Maximum-margin classifier (kernel method)
मौलिक स्रोत	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
उपनाम	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
संबंधित	5	4	5
सारांश	Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.