Variational Bayesian Learning of Cooperative Vector Quantizer Model - The Theory 
 

Source URL: people.cs.pitt.edu

• Statistics
• Statistical models
• Bayesian network
• Latent variable
• Variational Bayesian methods
• Independent component analysis
• Expectationmaximization algorithm
• Generative model
• Principal component analysis
• Bayesian inference
• Normal distribution
• Mixture model

CS168: The Modern Algorithmic Toolbox Lecture #8: How PCA Works Tim Roughgarden & Gregory Valiant∗ April 20,

Source URL: theory.stanford.edu

• Algebra
• Linear algebra
• Mathematics
• Matrices
• Matrix theory
• Numerical linear algebra
• Mathematical physics
• Eigenvalues and eigenvectors
• Principal component analysis
• Orthogonal matrix
• Singular value decomposition
• Diagonalizable matrix

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks arXiv:1312.6120v3 [cs.NE] 19 FebAndrew M. Saxe ()

Source URL: arxiv.org

• Artificial neural networks
• Statistics
• Machine learning
• Applied mathematics
• Deep learning
• Autoencoder
• Backpropagation
• Principal component analysis
• Matrix

IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 22, NO. 10, OCTOBERCodebook-Based Audio Feature Representation for Music Information Retrieval

Source URL: eceweb.ucsd.edu

• Algebra
• Mathematics
• Information science
• Machine learning
• Vector quantization
• Dimension reduction
• Search algorithms
• Feature learning
• Code
• K-means clustering
• Lasso
• Principal component analysis

Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons Lars Buesing, Wolfgang Maass Institute for Theoretical Computer Science

Source URL: www.gatsby.ucl.ac.uk

• Computational neuroscience
• Neuroscience
• Nervous system
• Artificial neural networks
• Statistics
• Multivariate statistics
• Biophysics
• Biological neuron model
• Principal component analysis
• Artificial neuron
• Spike-timing-dependent plasticity
• Hebbian theory

Microsoft Word - 285360_0_art_file_1_1138573116.doc

Source URL: climateaudit.files.wordpress.com

• Climate history
• Covariance and correlation
• Hockey stick graph
• Statistics
• Temperature record of the past 1000 years
• Steve McIntyre
• Principal component analysis
• Correlation and dependence
• M&M
• Covariance
• AW
• Mathematical analysis

The theoretical justifications for Principal Component Analysis (PCA) typically assume that the data is IID over the estimation window. In practice, this assumption is routinely violated in financial data. We examine the

Source URL: mmds-data.org

Appendix A: Face subclasses In Fig. 3, we showed an example of features versus context. In that example, we illustrated the idea with the class “eyes.” In Fig. S1 we show the subclasses for the class “face” and t

Source URL: www2.ece.ohio-state.edu

• Algebra
• Mathematics
• Abstract algebra
• Eigenvalues and eigenvectors
• Linear algebra
• Matrix theory
• Singular value decomposition
• Principal component analysis

LARGE-SCALE MUSIC SIMILARITY SEARCH WITH SPATIAL TREES Brian McFee Computer Science and Engineering University of California, San Diego ABSTRACT

Source URL: eceweb.ucsd.edu

• Statistics
• Search algorithms
• Information science
• Information
• Dimension reduction
• K-d tree
• K-means clustering
• Normal distribution
• K-nearest neighbors algorithm
• Principal component analysis
• Locality-sensitive hashing
• Nearest neighbor search

Semi-Supervised Dimensionality Reduction for Analyzing High-Dimensional Data with Constraints Su Yan∗∗ IBM Almaden Research Center, 650 Harry Rd San Jose, CA 95120, USA Sofien Bouaziz

Source URL: sofienbouaziz.com

• Algebra
• Mathematics
• Statistics
• Multivariate statistics
• Linear algebra
• Functional analysis
• Dimension reduction
• Geostatistics
• Kernel principal component analysis
• Nonlinear dimensionality reduction
• Principal component analysis
• Dimensionality reduction