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Contributions to unsupervised learning from massive high-dimensional data streams : structuring, hashing and clustering
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Title:
Contributions to unsupervised learning from massive high-dimensional data streams : structuring, hashing and clustering
Author:
Morvan, Anne
Subjects:
Apprentissage non supervisé
;
Approximation
;
Clustering
;
Dimensionality
reduction
;
Flux
;
Hachage
;
Hashing
;
Nearest neighbors search
;
Recherche des plus proches voisins
;
Réduction de dimension
;
Résumés minimalistes
;
Sketching
;
Streaming
;
Unsupervised learning
Description:
Cette thèse étudie deux tâches fondamentales d'apprentissage non supervisé: la recherche des plus proches voisins et le clustering de données massives en grande dimension pour respecter d'importantes contraintes de temps et d'espace.Tout d'abord, un nouveau cadre théorique permet de réduire le coût spatial et d'augmenter le débit de traitement du Cross-polytope LSH pour la recherche du plus proche voisin presque sans aucune perte de précision.Ensuite, une méthode est conçue pour apprendre en une seule passe sur des données en grande dimension des codes compacts binaires. En plus de garanties théoriques, la qualité des sketches obtenus est mesurée dans le cadre de la recherche approximative des plus proches voisins. Puis, un algorithme de clustering sans paramètre et efficace en terme de coût de stockage est développé en s'appuyant sur l'extraction d'un arbre couvrant minimum approché du graphe de dissimilarité compressé auquel des coupes bien choisies sont effectuées. This thesis focuses on how to perform efficiently unsupervised machine learning such as the fundamentally linked nearest neighbor search and clustering task, under time and space constraints for high-dimensional datasets. First, a new theoretical framework reduces the space cost and increases the rate of flow of data-independent Cross-polytope LSH for the approximative nearest neighbor search with almost no loss of accuracy.Second, a novel streaming data-dependent method is designed to learn compact binary codes from high-dimensional data points in only one pass. Besides some theoretical guarantees, the quality of the obtained embeddings are accessed on the approximate nearest neighbors search task.Finally, a space-efficient parameter-free clustering algorithm is conceived, based on the recovery of an approximate Minimum Spanning Tree of the sketched data dissimilarity graph on which suitable cuts are performed.
Creation Date:
2018
Language:
English
Source:
Theses.fr
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