2008
NIPS
NeurIPS 2008
Kernel Measures of Independence for non-iid Data
Abstract
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space embeddings of distributions. We apply this new criterion to independent component analysis and sequence clustering.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— undirected graphical models
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Hot Topic Early Bird
— graphical model
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy
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Trend Setter
— Information Theory
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Stochastic Processes
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Mathematics > Information Theory
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Graphical Models
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Bayesian & Probabilistic > Kernel Methods