Auto-weighted multi-view learning for image clustering and semi-supervised classification
Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …
oriented methods have been widely investigated and achieve promising performance …
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 …
Extrinsic methods for coding and dictionary learning on Grassmann manifolds
Sparsity-based representations have recently led to notable results in various visual
recognition tasks. In a separate line of research, Riemannian manifolds have been shown …
recognition tasks. In a separate line of research, Riemannian manifolds have been shown …
Fusion of centroid-based clustering with graph clustering: An expectation-maximization-based hybrid clustering
Z Uykan - IEEE Transactions on Neural Networks and Learning …, 2021 - ieeexplore.ieee.org
This article extends the expectation-maximization (EM) formulation for the Gaussian mixture
model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of …
model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of …
Advances in computational and statistical diffusion MRI
Computational methods are crucial for the analysis of diffusion magnetic resonance imaging
(MRI) of the brain. Computational diffusion MRI can provide rich information at many size …
(MRI) of the brain. Computational diffusion MRI can provide rich information at many size …
Coding kendall's shape trajectories for 3D action recognition
A Ben Tanfous, H Drira… - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
Suitable shape representations as well as their temporal evolution, termed trajectories, often
lie to non-linear manifolds. This puts an additional constraint (ie, non-linearity) in using …
lie to non-linear manifolds. This puts an additional constraint (ie, non-linearity) in using …
Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease
Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis
and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging …
and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging …
Sparse coding of shape trajectories for facial expression and action recognition
AB Tanfous, H Drira, BB Amor - IEEE transactions on pattern …, 2019 - ieeexplore.ieee.org
The detection and tracking of human landmarks in video streams has gained in reliability
partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying …
partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying …
Learnable subspace clustering
This article studies the large-scale subspace clustering (LS 2 C) problem with millions of
data points. Many popular subspace clustering methods cannot directly handle the LS 2 C …
data points. Many popular subspace clustering methods cannot directly handle the LS 2 C …
Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis
White matter characterization studies use the information provided by diffusion magnetic
resonance imaging (dMRI) to draw cross-population inferences. However, the structure …
resonance imaging (dMRI) to draw cross-population inferences. However, the structure …