6 citations · 16 across the 10 of their papers we have counts for
10 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…
Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning
Babak Hosseini, Barbara Hammer
Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has b…
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series
Babak Hosseini, Barbara Hammer
There exist many approaches for description and recognition of unseen classes in datasets. Nevertheless, it becomes a challenging problem when we deal with multivariate time-series…
Feasibility Based Large Margin Nearest Neighbor Metric Learning
Babak Hosseini, Barbara Hammer
Large margin nearest neighbor (LMNN) is a metric learner which optimizes the performance of the popular NN classifier. However, its resulting metric relies on pre-selected targe…
Efficient Metric Learning for the Analysis of Motion Data
Babak Hosseini, Barbara Hammer
We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture da…