We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Projections, Learning, and Sparsity for Efficient Data Processing

Formale Metadaten

Titel
Projections, Learning, and Sparsity for Efficient Data Processing
Serientitel
Teil
9
Anzahl der Teile
10
Autor
Lizenz
CC-Namensnennung 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
Identifikatoren
Herausgeber
Erscheinungsjahr
Sprache

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
The talk will discuss recent generalizations of sparse recovery guarantees and compressive sensing to the context of machine learning. Assuming some "low-dimensional model" on the probability distribution of the data, we will see that in certain scenarios it is indeed (empirically) possible to compress a large data-collection into a reduced representation, of size driven by the complexity of the learning task, while preserving the essential information necessary to process it. Two case studies will be given: compressive clustering, and compressive Gaussian Mixture Model estimation, with an illustration on large-scale model-based speaker verification. Time allowing, some recent results on compressive spectral clustering will also be discussed.