Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
GRIBONVAL, Rémi
Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio [PANAMA]
Dynamic Networks : Temporal and Structural Capture Approach [DANTE]
Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio [PANAMA]
Dynamic Networks : Temporal and Structural Capture Approach [DANTE]
BLANCHARD, Gilles
Laboratoire de Mathématiques d'Orsay [LMO]
Understanding the Shape of Data [DATASHAPE]
See more >
Laboratoire de Mathématiques d'Orsay [LMO]
Understanding the Shape of Data [DATASHAPE]
GRIBONVAL, Rémi
Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio [PANAMA]
Dynamic Networks : Temporal and Structural Capture Approach [DANTE]
Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio [PANAMA]
Dynamic Networks : Temporal and Structural Capture Approach [DANTE]
BLANCHARD, Gilles
Laboratoire de Mathématiques d'Orsay [LMO]
Understanding the Shape of Data [DATASHAPE]
< Reduce
Laboratoire de Mathématiques d'Orsay [LMO]
Understanding the Shape of Data [DATASHAPE]
Language
en
Article de revue
This item was published in
Mathematical Statistics and Learning. 2021-08-21, vol. 3, n° 2, p. 165–257
EMS Publishing House
English Abstract
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a ...Read more >
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a companion paper.The principle of compressive statistical learning is to compress a training collection, in one pass, into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task. We explicitly describe and analyze random feature functions which empirical averages preserve the needed information for compressive clustering and compressive Gaussian mixture modeling with fixed known variance, and establish sufficient sketch sizes given the problem dimensions.Read less <
English Keywords
random moments
random features
clustering
mixture modeling
principal component analysis.
principal component analysis
dimension reduction
unsupervised learning
kernel mean embedding
statistical learning
European Project
PLEASE: Projections, Learning, and Sparsity for Efficient data-processing
ANR Project
Approches statistiquement et computationnellement efficicaces pour l'intelligence artificielle - ANR-19-CHIA-0021
Algorithmes, Approximations, Parcimonie et Plongements pour l'IA - ANR-19-CHIA-0009
Algorithmes, Approximations, Parcimonie et Plongements pour l'IA - ANR-19-CHIA-0009
Origin
Hal imported