Non-St ationary Analysis on Datasets and Applications

منو

" Non-St ationary Analysis on Datasets and Applications "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	149965
Doc. No	:	ET21757
Main Entry	:	Arthur Szlam
Title Proper	:	Non-St ationary Analysis on Datasets and Applications
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	In the first part of this thesis, we study the use of anisotropic diffusions on datasetsas a tool for signal processing and machine learning. We modify the geometry of thedata by adding feature coordinates derived from a function or set of functions which areto be studied. The anisotropic diffusion on the data is just the isotropic diffusion on itsmodification. We thus transfer some of the complexity of the functions to the geometryof the dataset. This gives a notion of smoothness on the dataset which is adapted to thefunction(s) under study. We show applications to image denoising and semi-supervisedlearning problems.In the second part, we study the construction of local and multiscale bases on datasetswhich are adapted to dyadic partitions of the dataset. Some progress is made towardsgeneralizing the local cosines..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL44939.pdf
Title and statement of responsibility and	:	Non-St ationary Analysis on Datasets and Applications [Thesis]