Riemannian approaches in brain-computer interfaces: a review
Although promising from numerous applications, current brain-computer interfaces (BCIs)
still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and …
still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and …
Log-determinant divergences revisited: Alpha-beta and gamma log-det divergences
This work reviews and extends a family of log-determinant (log-det) divergences for
symmetric positive definite (SPD) matrices and discusses their fundamental properties. We …
symmetric positive definite (SPD) matrices and discusses their fundamental properties. We …
Riemannian dictionary learning and sparse coding for positive definite matrices
Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas
of computer vision and machine learning. While these matrices form an open subset of the …
of computer vision and machine learning. While these matrices form an open subset of the …
Kernel sparse subspace clustering on symmetric positive definite manifolds
Sparse subspace clustering (SSC), as one of the most successful subspace clustering
methods, has achieved notable clustering accuracy in computer vision tasks. However, SSC …
methods, has achieved notable clustering accuracy in computer vision tasks. However, SSC …
Riemannian coding and dictionary learning: Kernels to the rescue
M Harandi, M Salzmann - Proceedings of the IEEE Conference on …, 2015 - cv-foundation.org
While sparse coding on non-flat Riemannian manifolds has recently become increasingly
popular, existing solutions either are dedicated to specific manifolds, or rely on optimization …
popular, existing solutions either are dedicated to specific manifolds, or rely on optimization …
Second-order convolutional neural networks
K Yu, M Salzmann - arXiv preprint arXiv:1703.06817, 2017 - arxiv.org
Convolutional Neural Networks (CNNs) have been successfully applied to many computer
vision tasks, such as image classification. By performing linear combinations and element …
vision tasks, such as image classification. By performing linear combinations and element …
Geometry-aware principal component analysis for symmetric positive definite matrices
I Horev, F Yger, M Sugiyama - Asian Conference on Machine …, 2016 - proceedings.mlr.press
Symmetric positive definite (SPD) matrices, eg covariance matrices, are ubiquitous in
machine learning applications. However, because their size grows as n^ 2 (where n is the …
machine learning applications. However, because their size grows as n^ 2 (where n is the …
Statistically-motivated second-order pooling
K Yu, M Salzmann - Proceedings of the European …, 2018 - openaccess.thecvf.com
However, the nature of such operations is usually computationally expensive, and resulting
vector representation orders of magnitude larger than first-order baselines. Here, by …
vector representation orders of magnitude larger than first-order baselines. Here, by …
Sparse representation over learned dictionaries on the Riemannian manifold for automated grading of nuclear pleomorphism in breast cancer
Breast cancer is found to be the most pervasive type of cancer among women. Computer
aided detection and diagnosis of cancer at the initial stages can increase the chances of …
aided detection and diagnosis of cancer at the initial stages can increase the chances of …
Online dictionary learning on symmetric positive definite manifolds with vision applications
Abstract Symmetric Positive Definite (SPD) matrices in the form of region covariances are
considered rich descriptors for images and videos. Recent studies suggest that exploiting …
considered rich descriptors for images and videos. Recent studies suggest that exploiting …