LEARNING RANK REDUCED MAPPINGS USING CANONICAL CORRELATION ANALYSIS
2016 (English)In: 2016 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), IEEE , 2016Conference paper (Refereed)
Correspondence relations between different views of the same scene can be learnt in an unsupervised manner. We address autonomous learning of arbitrary fixed spatial (point-to-point) mappings. Since any such transformation can be represented by a permutation matrix, the signal model is a linear one, whereas the proposed analysis method, mainly based on Canonical Correlation Analysis (CCA) is based on a generalized eigensystem problem, i.e., a nonlinear operation. The learnt transformation is represented implicitly in terms of pairs of learned basis vectors and does neither use nor require an analytic/parametric expression for the latent mapping. We show how the rank of the signal that is shared among views may be determined from canonical correlations and how the overlapping (=shared) dimensions among the views may be inferred.
Place, publisher, year, edition, pages
IEEE , 2016.
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-134108DOI: 10.1109/SSP.2016.7551728ISI: 000390840200024ISBN: 978-1-4673-7802-4 (print)OAI: oai:DiVA.org:liu-134108DiVA: diva2:1067517
19th IEEE Statistical Signal Processing Workshop (SSP)