Approximate Nearest Neighbors Search in High Dimensions and Locality-Sensitive Hashing PAPERS Piotr Indyk, Rajeev Motwani: Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality, STOC 1998.

Source URL: graphics.stanford.edu

SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces Josua Krause, Aritra Dasgupta, Jean-Daniel Fekete, and Enrico Bertini Abstract— Dealing with the curse of dimensio

Source URL: josuakrause.github.io

Challenge On Online Time Series Clustering For Demand Response OPTIC - A Theory to Break the ‘Curse of Dimensionality’ Ranjan Pal, Charalampos Chelmis, Chandra Tadepalli Marc Frincu, Saima Aman, Viktor Prasanna Unive

Source URL: ganges.usc.edu

Selective Hashing: Closing the Gap between Radius Search and k-NN Search Jinyang Gao‡ H. V. Jagadish§

Source URL: www.comp.nus.edu.sg

• Search algorithms
• Information science
• Information retrieval
• Mathematics
• Nearest neighbor search
• Locality-sensitive hashing
• K-nearest neighbors algorithm
• Hash function
• Hash table
• Curse of dimensionality
• Similarity search

EUSIPCO THE RELATION OF HUBS TO THE DODDINGTON ZOO IN SPEAKER VERIFICATION Dominik Schnitzer, Arthur Flexer, Jan Schl¨uter Austrian Research Institute for Artificial Intelligence (OFAI) Freyung 6/6, 101

Source URL: www.eurasip.org

• Machine learning
• Learning
• Artificial intelligence
• Automatic identification and data capture
• Numerical analysis
• Biometrics
• Speaker recognition
• Speech processing
• Curse of dimensionality
• Hub
• Information
• Normalization

Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series Hoang-Vu Nguyen◦ Jilles Vreeken◦

Source URL: eda.mmci.uni-saarland.de

• Statistics
• Multivariate statistics
• Machine learning
• Dimension reduction
• Principal component analysis
• Numerical analysis
• Dimensionality reduction
• Curse of dimensionality
• KullbackLeibler divergence
• Divergence
• Pattern recognition
• Normal distribution

Using mutual proximity for novelty detection in audio music similarity Arthur Flexer? and Dominik Schnitzer Austrian Research Institute for Artificial Intelligence Freyung 6/6, Vienna, Austria

Source URL: www.ecmlpkdd2013.org

• Search algorithms
• Data mining
• K-nearest neighbors algorithm
• Machine learning
• Numerical analysis
• Statistical tests
• Outlier
• Receiver operating characteristic
• XTR
• Statistical hypothesis testing
• Normal distribution
• Curse of dimensionality

Multi-objective Genetic Programming for Visual Analytics Ilknur Icke1 and Andrew Rosenberg1,2 1 The Graduate Center, The City University of New York,

Source URL: eniac.cs.qc.cuny.edu

• Multivariate statistics
• Dimension reduction
• Statistical classification
• Machine learning
• Principal component analysis
• Dimensionality reduction
• K-nearest neighbors algorithm
• Feature extraction
• Curse of dimensionality
• Linear discriminant analysis
• Multidimensional scaling
• Kernel principal component analysis

CS168: The Modern Algorithmic Toolbox Lecture #4: Dimensionality Reduction Tim Roughgarden & Gregory Valiant April 8,

Source URL: theory.stanford.edu

• Machine learning
• Random projection
• MinHash
• Nearest neighbor search
• K-nearest neighbors algorithm
• Dimensionality reduction
• Normal distribution
• Jaccard index
• Curse of dimensionality
• Standard deviation
• Nonlinear dimensionality reduction

Time Series Prediction Jaakko Hollm´en Helsinki Institute for Information Techhnology Aalto University, Department of Computer Science Espoo, Finland Web: http://users.ics.aalto.fi/jhollmen/

Source URL: www.spaceclimate.fi

• Machine learning
• Numerical analysis
• Curse of dimensionality
• Dynamic programming
• Ogonek
• Data mining
• Prediction
• Time series