Bronstein, Michael Kovnatsky, Artiom 2016-05-19 Spectral methods have proven themselves as an important and versatile tool in a wide range of problems in the fields of computer graphics, machine learning, pattern recognition, and computer vision, where many important problems boil down to constructing a Laplacian operator and finding a few of its eigenvalues and eigenfunctions. Classical examples include the computation of diffusion distances on manifolds in computer graphics, Laplacian eigenmaps, and spectral clustering in machine learning. In many cases, one has to deal with multiple data spaces simultaneously. For example, clustering multimedia data in machine learning applications involves various modalities or ``views'' (e.g., text and images), and finding correspondence between shapes in computer graphics problems is an operation performed between two or more modalities. In this thesis, we develop a generalization of spectral methods to deal with multiple data spaces and apply them to problems from the domains of computer graphics, machine learning, and image processing. Our main construction is based on simultaneous diagonalization of Laplacian operators. We present an efficient numerical technique for computing joint approximate eigenvectors of two or more Laplacians in challenging noisy scenarios, which also appears to be the first general non-smooth manifold optimization method. Finally, we use the relation between joint approximate diagonalizability and approximate commutativity of operators to define a structural similarity measure for images. We use this measure to perform structure-preserving color manipulations of a given image. application/pdf https://localhost:5000/ark:/12658/srd1318738 https://susi.usi.ch/global/documents/318738 https://susi.usi.ch/documents/318738/files/2016INFO006.pdf eng info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:rero-006-115648 info:eu-repo/semantics/altIdentifier/ark/12658/srd1318738 info:eu-repo/semantics/openAccess License undefined Data analysis Multimodal data Spectral methods Joint diagonalization Multimodal clustering Manifold correspondence Functional map Color manipulation Color adaptation Pose transferring Shape processing Shape analysis info:eu-repo/classification/udc/004 Spectral methods for multimodal data analysis http://purl.org/coar/resource_type/c_db06