Multiclass Spectral Clustering Stella X. Yu Robotics Institute and CNBC Carnegie Mellon University Pittsburgh, PA

Source URL: www1.icsi.berkeley.edu

• Linear algebra
• Matrix theory
• Functional analysis
• Control theory
• Numerical analysis
• Eigenvalues and eigenvectors
• Spectral clustering
• Singular value decomposition
• Discretization
• Matrix
• Gaussian function
• KarhunenLove theorem

Discussion of “Spectral Dimensionality Reduction via Maximum Entropy” Laurens van der Maaten Delft University of Technology

Source URL: lvdmaaten.github.io

• Multivariate statistics
• Dimension reduction
• Computational statistics
• Machine learning
• Dimension
• Nonlinear dimensionality reduction
• Dimensionality reduction
• Isomap
• Semidefinite embedding
• Spectral clustering
• Kernel principal component analysis
• T-distributed stochastic neighbor embedding

An Interactive Visual Testbed System for Dimension Reduction and Clustering of Large-scale High-dimensional Data Jaegul Choo, Hanseung Lee, Zhicheng Liu, John Stasko, and Haesun Park Georgia Institute of Technology, USA

Source URL: www.zcliu.org

• Visualization
• Multivariate statistics
• Dimension reduction
• Data
• Data visualization
• Information technology governance
• K-means clustering
• Parallel coordinates
• Principal component analysis
• Information visualization
• Spectral clustering
• Data analysis

Information Theoretic Clustering using Minimum Spanning Trees Andreas C. M¨ uller?1 , Sebastian Nowozin2 , and Christoph H. Lampert3 1

Source URL: amueller.github.io

• Cluster analysis
• Hierarchical clustering
• Network analysis
• Mutual information
• K-means clustering
• Image segmentation
• Pattern recognition
• Information bottleneck method
• Minimum spanning tree
• Spectral clustering
• Single-linkage clustering
• Determining the number of clusters in a data set

Unsupervised 3D Object Discovery and Categorization for Mobile Robots Jiwon Shin Rudolph Triebel

Source URL: europa.informatik.uni-freiburg.de

• Image segmentation
• Cluster analysis
• Categorization
• K-means clustering
• Spectral clustering
• KadirBrady saliency detector

Fully Connected Object Proposals for Video Segmentation Federico Perazzi1,2 Oliver Wang2 1 ETH Zurich

Source URL: www.ahornung.net

• Image segmentation
• IEEE conferences
• Computer vision
• Background subtraction
• Conference on Computer Vision and Pattern Recognition
• Support vector machine
• Conditional random field
• GrabCut
• International Conference on Computer Vision
• Optical flow
• Spectral clustering
• Histogram of oriented gradients

Spectral clustering is a well-known way to partition a graph or network into clusters or communities with provable guarantees on the quality of the clusters. This guarantee is known as the Cheeger inequality and it holds

Source URL: mmds-data.org

SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering Han Zhao† , Pascal Poupart† , Yongfeng Zhang§ and Martin Lysy‡ † David R. Cheriton School of Computer Science, University of Waterloo, Canada D

Source URL: yongfeng.me

• Multivariate statistics
• Linear algebra
• Matrix theory
• Non-negative matrix factorization
• Cluster analysis
• Spectral clustering
• K-means clustering
• Document clustering
• Pattern recognition
• Expectationmaximization algorithm
• Determining the number of clusters in a data set
• Hierarchical clustering

Local Spectral Diffusion for Robust Community Detection ∗ Kun He, Pan Shi Huazhong University of

Source URL: www.mlgworkshop.org

• Algebraic graph theory
• Matrices
• Matrix theory
• Markov models
• Networks
• Graph partition
• Laplacian matrix
• PageRank
• Community structure
• Spectral clustering
• Conductance
• Random walk

PHYSICAL REVIEW E 93, Spectral-clustering approach to Lagrangian vortex detection Alireza Hadjighasem,1,* Daniel Karrasch,1,† Hiroshi Teramoto,2,‡ and George Haller1,§ 1

Source URL: georgehaller.com