Mitigating the popularity bias of graph collaborative filtering: A dimensional collapse perspective
Abstract Graph-based Collaborative Filtering (GCF) is widely used in personalized
recommendation systems. However, GCF suffers from a fundamental problem where …
recommendation systems. However, GCF suffers from a fundamental problem where …
An overview and empirical comparison of distance metric learning methods
P Moutafis, M Leng, IA Kakadiaris - IEEE transactions on …, 2016 - ieeexplore.ieee.org
In this paper, we first offer an overview of advances in the field of distance metric learning.
Then, we empirically compare selected methods using a common experimental protocol …
Then, we empirically compare selected methods using a common experimental protocol …
On the Jensen–Shannon symmetrization of distances relying on abstract means
F Nielsen - Entropy, 2019 - mdpi.com
The Jensen–Shannon divergence is a renowned bounded symmetrization of the
unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler …
unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler …
Review of Riemannian distances and divergences, applied to SSVEP-based BCI
The firstgeneration of brain-computer interfaces (BCI) classifies multi-channel
electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second …
electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second …
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
Simulating complex physical systems often involves solving partial differential equations
(PDEs) with some closures due to the presence of multi-scale physics that cannot be fully …
(PDEs) with some closures due to the presence of multi-scale physics that cannot be fully …
Dimensionality reduction on SPD manifolds: The emergence of geometry-aware methods
M Harandi, M Salzmann… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Representing images and videos with Symmetric Positive Definite (SPD) matrices, and
considering the Riemannian geometry of the resulting space, has been shown to yield high …
considering the Riemannian geometry of the resulting space, has been shown to yield high …
From manifold to manifold: Geometry-aware dimensionality reduction for SPD matrices
Representing images and videos with Symmetric Positive Definite (SPD) matrices and
considering the Riemannian geometry of the resulting space has proven beneficial for many …
considering the Riemannian geometry of the resulting space has proven beneficial for many …
Power normalizations in fine-grained image, few-shot image and graph classification
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 …
in classification problems. We study PNs in the deep learning setup via a novel PN layer …
Structure-preserving image smoothing via region covariances
Recent years have witnessed the emergence of new image smoothing techniques which
have provided new insights and raised new questions about the nature of this well-studied …
have provided new insights and raised new questions about the nature of this well-studied …
Unsupervised learning discriminative MIG detectors in nonhomogeneous clutter
Principal component analysis (PCA) is a commonly used pattern analysis method that maps
high-dimensional data into a lower-dimensional space maximizing the data variance, that …
high-dimensional data into a lower-dimensional space maximizing the data variance, that …