2006
NIPS
NeurIPS 2006
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
Abstract
We design an on-line algorithm for Principal Component Analysis. In each trial the current instance is projected onto a probabilistically chosen low dimensional subspace. The total expected quadratic approximation error equals the total quadratic approximation error of the best subspace chosen in hindsight plus some additional term that grows linearly in dimension of the subspace but logarithmically in the dimension of the instances.
🚀
Conference Pioneer
— NIPS 2006
🧭
Keyword Pioneer
— regret bound
🐣
Hot Topic Early Bird
— online learning
🐝
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, Speech & Audio
🌱
Topic Pioneer
— Kernel Methods
📈
Trend Setter
— Online Algorithms
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Learning Types > Online Learning
Machine Learning > Optimization & Theory > Online Algorithms
Machine Learning > Optimization & Theory > Kernel Methods