6 citations · 11 across the 7 of their papers we have counts for
8 papers
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation
Babak Hosseini, Romain Montagne, Barbara Hammer
Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks. Hence, many skeleton-based action reco…
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection
Babak Hosseini, Barbara Hammer
Prototype-based methods are of the particular interest for domain specialists and practitioners as they summarize a dataset by a small set of representatives. Therefore, in a class…
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
Babak Hosseini, Barbara Hammer
Dimensionality reduction (DR) on the manifold includes effective methods which project the data from an implicit relational space onto a vectorial space. Regardless of the achievem…
Non-Negative Local Sparse Coding for Subspace Clustering
Babak Hosseini, Barbara Hammer
Subspace sparse coding (SSC) algorithms have proven to be beneficial to clustering problems. They provide an alternative data representation in which the underlying structure of th…
Confident Kernel Sparse Coding and Dictionary Learning
Babak Hosseini, Barbara Hammer
In recent years, kernel-based sparse coding (K-SRC) has received particular attention due to its efficient representation of nonlinear data structures in the feature space. Neverth…
Non-Negative Kernel Sparse Coding for the Classification of Motion Data
Babak Hosseini, Felix Hülsmann, Mario Botsch +1
We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks…