পদ্ধতির তুলনা করুন

	কার্নেল পিসিএ	অটোএনকোডার
ক্ষেত্র	যন্ত্র শিখন	গভীর শিখন
পরিবার	Latent structure	Machine learning
উদ্ভবের বছর	1998	2006
প্রবর্তক	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Hinton, G.E. & Salakhutdinov, R.R.
ধরন	Nonlinear dimensionality reduction via kernel trick	Neural network (encoder-decoder)
মৌলিক উৎস	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.
অপর নাম	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network
সম্পর্কিত	5	4
সারসংক্ষেপ	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.