We 1 develop some theoreti al results for a robust similarity measure named generalized minmax (GMM). This similarity has dire t appli ations in ma hine learning as a positive de nite kernel and an be e iently omputed via probabilisti hashing. Owing to the dis rete nature, the hashed values an also be used for e ient near neighbor sear h. We prove the theoreti al limit of GMM and the onsisten y...

We propose the “generalized min-max” (GMM) kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named “generalized consistent weighted sampling” (GCWS) which linearizes this (nonlinear) kernel. A natural competitor of GMM is the radial basis function (RBF) kernel, whose correspond...

Our previous experiments in Text-Dependent and -Independent Speaker Verification (TD-SV and TI-SV) using trajectory-based models, showed that non-stationary segments benefit TD-SV but not TI-SV, because in TI-SV maximum likelihood (ML) training results mainly in stationary segments. This result questions the role of non-stationary, ‘delta’ parameters in conventional GMM-based TI-SV. In this pap...

Tsetse flies (Glossina spp.) are the cyclical vectors of Trypanosoma spp., which are unicellular parasites responsible for multiple diseases, including nagana in livestock and sleeping sickness in humans in Africa. Glossina species, including Glossina morsitans morsitans (Gmm), for which the Whole Genome Sequence (WGS) is now available, have established symbiotic associations with three endosym...

Point set registration is to determine correspondences between two different point sets, then recover the spatial transformation between them. Many current methods, become extremely slow as the cardinality of the point set increases; making them impractical for large point sets. In this paper, we propose a bi-stage method called bi-GMMTPS, based on Gaussian Mixture Models and Thin-Plate Splines...

We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, a lowrank GMM is imposed on the local image patches. This lowrank GMM is derived via eigenvalue thresholding of the GMM trained on the projection of the measurement data, thus l...