Manifold learning algorithms aim to recover the underlying lowdimensional parametrization of the data using either local or global features. It is however widely recognized that the low dimensional parametrizations will

Source URL: mmds-data.org

• Machine learning
• Cybernetics
• Learning
• Manifold
• Dimension reduction
• Multivariate statistics
• Nonlinear dimensionality reduction

Statistical Inference, Learning and Models in Big Data Franke, Beate; Plante, Jean–François; Roscher, Ribana; Lee, Annie; Smyth, Cathal; Hatefi, Armin; Chen, Fuqi; Gil, Einat; Schwing, Alexander; Selvitella, Aless

Source URL: www.ipb.uni-bonn.de

• Machine learning
• Data management
• Multivariate statistics
• Big data
• Technology forecasting
• Transaction processing
• Feature learning
• Nonlinear dimensionality reduction

Journal of Machine Learning Research450 Submitted 11/07; Revised 11/09; Published 1/10 Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting

Source URL: www.cs.stevens.edu

• Multivariate statistics
• Dimension reduction
• Computational statistics
• Machine learning
• Isomap
• Dimensionality reduction
• Nonlinear dimensionality reduction
• Semidefinite embedding
• Principal component analysis
• Tensor
• Manifold
• Multidimensional scaling

Interacting with Massive Behavioral Data Shih-Chieh Su Qualcomm IncMorehouse Drive San Diego, CAUSA

Source URL: poloclub.gatech.edu

• Multivariate statistics
• Multidimensional scaling
• Dimensionality reduction
• Isomap
• T-distributed stochastic neighbor embedding
• Principal component analysis
• Algorithm
• Data visualization
• Nonlinear dimensionality reduction

Visual Interactive Neighborhood Mining on High Dimensional Data Emin Aksehirli • Bart Goethals •

Source URL: poloclub.gatech.edu

• Multivariate statistics
• Cluster analysis
• Data mining
• Geostatistics
• K-means clustering
• Hierarchical clustering
• Dimension reduction
• K-nearest neighbors algorithm
• Machine learning
• Multidimensional scaling
• Pattern recognition
• Nonlinear dimensionality reduction

Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms David F. Gleich Computer Science Department Purdue University West Lafayette, IN

Source URL: www.stat.berkeley.edu

• Graph theory
• Mathematics
• NP-complete problems
• Graph partition
• Connectivity
• Graph coloring
• Graph
• Nonlinear dimensionality reduction
• Vertex
• SPQR tree
• Signed graph

Interactive Exploration of Larger Pattern Collections: A Case Study on a Cocktail Dataset

Source URL: poloclub.gatech.edu

• Multivariate statistics
• Statistics
• Dimension reduction
• Data mining
• Information
• Formal sciences
• Dimension
• Nonlinear dimensionality reduction
• Data analysis
• Principal component analysis
• Association rule learning
• Multidimensional scaling

review f o c u s o n b i g data Dimensionality reduction for large-scale neural recordings

Source URL: stat.columbia.edu

• Computational neuroscience
• Neuroscience
• Neural networks
• Multivariate statistics
• Neural coding
• Neural oscillation
• Principal component analysis
• Dimensionality reduction
• Artificial neural network
• Nonlinear dimensionality reduction
• Biological neural network
• Neural ensemble

Reduced order modelling & nonlinear dimensionality reduction for real time computational mechanics with emphasis in computational surgery Francisco (Paco) Chinesta ICI – Institut de Calcul Intensif @ Ecole Centrale de

Source URL: www-timc.imag.fr

Microsoft Word - han_giw_2008_final.doc

Source URL: www.jsbi.org

• Multivariate statistics
• Statistics
• Machine learning
• Learning
• Statistical classification
• Dimension reduction
• Linear algebra
• Non-negative matrix factorization
• Support vector machine
• Kernel principal component analysis
• Principal component analysis
• Nonlinear dimensionality reduction