Abstract Principal Component Analysis (PCA) is one of the most widely used data analysis
methods in machine learning and AI. This manuscript focuses on the mathematical …

Spatial transcriptomic studies are becoming increasingly common and large, posing
important statistical and computational challenges for many analytic tasks. Here, we present …

The problem of cross-modal retrieval from multimedia repositories is considered. This
problem addresses the design of retrieval systems that support queries across content …

The success of kernel methods has initiated the design of novel positive semidefinite
functions, in particular for structured data. A leading design paradigm for this is the …

Straightforward classification using kernelized SVMs requires evaluating the kernel for a test
vector and each of the support vectors. For a class of kernels we show that one can do this …

In this paper, we describe the results of a channel measurement and modeling campaign for
the vehicle-to-vehicle (V2V) channel in the 5-GHz band. We describe measurements and …

Most successful object recognition systems rely on binary classification, deciding only if an
object is present or not, but not providing information on the actual object location. To …

We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime
and memory complexity that is independent of the number of support vectors. This class of …

Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances
in classification problems. We study PNs in the deep learning setup via a novel PN layer …

Emerging Internet services and applications attract increasing users to involve in diverse
video-related activities, such as video searching, video downloading, video sharing and so …