Live briefing
Everything about Manifold alignment
Class of machine learning algorithms
Manifold alignment is a class of machine learning algorithms that produce projections between sets of data, given that the original data sets lie on a common manifold. The concept was first introduced as such by Ham, Lee, and Saul in 2003, adding a manifold constraint to the general problem of correlating sets of high-dimensional vectors. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Manifold alignmentA 5-minute orientation
- Skim a recent paper: Domain Adaptive Algorithm Based on Multi-Manifold Embedded Distributed Alignment for Brain-Computer InterfacesInfluential work since 2022
- Pick up a classic book: Manifold learning theory and applicationsThe most-reprinted book on the subject
- Read the landmark paper: Semisupervised Manifold Alignment of Multimodal Remote Sensing ImagesCited 131 times
- Go sideways: Nonlinear dimensionality reductionA closely related topic
- Go sideways: Diffusion waveletsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images
Discriminative Manifold Distribution Alignment for Domain Adaptation
Mapping Users across Networks by Manifold Alignment on Hypergraph
Multi-view manifold learning with locality alignment
Kernel Manifold Alignment for Domain Adaptation
Distribution Adaptation and Manifold Alignment for complex processes fault diagnosis
GrassMA: Graph-Based Semi-Supervised Manifold Alignment for Indoor WLAN Localization
An improved local tangent space alignment method for manifold learning
🆕 Influential since 2022
Domain Adaptive Algorithm Based on Multi-Manifold Embedded Distributed Alignment for Brain-Computer Interfaces
Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment
Locally alignment based manifold learning for simultaneous feature selection and extraction in classification problems
Manifold Alignment-Based Nonintrusive and Nonlinear Multifidelity Reduced-Order Modeling
Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling
📚 Books worth reading
- 📕Manifold learning theory and applicationsYunqian Ma, Yun Fu · 2011
Information Processing in Medical ImagingJames C. Gee · 2013- 📕Pipelines 2018Ont.) Pipelines (Conference) (2018 Toronto · 2018
Crane, Crawler Mounted, Gasoline, 3/4 Cu. Yd., With Attachments, Koehring, Model 304 With Chrysler Engine ModificationUnited States War Department · 1943
Diesel engine maintenance training manual, U.S. Navy. February, 1946United States. Navy Dept. Bureau of Ships. · 1946
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Nonlinear dimensionality reduction Nonlinear dimensionality reduction (NLDR), also known as manifold learning, is any of various related techniques that aim to project high-dimensional data…
- Diffusion wavelets Diffusion wavelets are a fast multiscale framework for the analysis of functions on discrete (or discretized continuous) structures like graphs, manifolds…
- Kernel method In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These…
- Support vector machine In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that…
- EM algorithm and GMM model In statistics, EM (expectation maximization) algorithm handles latent variables, while GMM is the Gaussian mixture model. In the picture below, are shown…
- Autoencoder An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns…
- Dimensionality reduction Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the…
- Kernel embedding of distributions In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which…