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Found 10 papers, 1 papers with code
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation
Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks.
Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection
In this paper, we propose a novel interpretable multiple-kernel prototype learning (IMKPL) to construct highly interpretable prototypes in the feature space, which are also efficient for the discriminative representation of the data.
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
In this research, we propose the interpretable kernel DR algorithm (I-KDR) as a new algorithm which maps the data from the feature space to a lower dimensional space where the classes are more condensed with less overlapping.
Confident Kernel Sparse Coding and Dictionary Learning
In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space.
Non-Negative Local Sparse Coding for Subspace Clustering
The NLSSC algorithm is also formulated in the kernel-based framework (NLKSSC) which can represent the nonlinear structure of data.
Non-Negative Kernel Sparse Coding for the Classification of Motion Data
We are interested in the decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing.
Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning
Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space.
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series
Furthermore, we obtain sparse encodings for unseen classes based on the learned MKD attributes, and upon which we propose a simple but effective incremental clustering algorithm to categorize the unseen MTS classes in an unsupervised way.
Feasibility Based Large Margin Nearest Neighbor Metric Learning
In this paper, we address the feasibility of LMNN's optimization constraints regarding these target points, and introduce a mathematical measure to evaluate the size of the feasible region of the optimization problem.
Efficient Metric Learning for the Analysis of Motion Data
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 data.
