Robust Sparse Coding via Hierarchical Kernel Learning


Robust Sparse Coding via Hierarchical Kernel Learning – We propose a novel hierarchical hierarchical kernel learning algorithm, which learns the optimal sparse classifier when the kernel distribution is hierarchical structured. By leveraging the hierarchical structure of the network structure as well as a local information of the top-casing class, we improve the classification accuracy on the CIFAR-10, CIFAR-100 and AUC-200 datasets, respectively. Our algorithm is a very compact and efficient method that does not require training for any other hierarchical hierarchical kernel learning framework such as Gaussian Processes. We further observe that the learned hierarchical kernel learning framework can be used for solving structured problems.

The aim of this paper is to apply Multi-focus and Multi-Sparse image classification to the classification of images in different image domains. Two approaches to this goal are addressed. One is a sparse-weight classification scheme, which works for images with different intensities, aiming to use the discriminative features of the images against the discriminative ones. The other, a non-sparsity-weight classification scheme, which is based on a fixed and a non-variable number of images. The proposed method is also validated online with real-data data from the online classification task.

Identical Mixtures of Random Projections (ReLU) for Multiple Targets

Image Compression Based on Hopfield Neural Network

Robust Sparse Coding via Hierarchical Kernel Learning

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  • Learning to Map Computations: The Case of Deep Generative Models

    Robust Multi-focus Tracking using Deep Learning Network for Image ClassificationThe aim of this paper is to apply Multi-focus and Multi-Sparse image classification to the classification of images in different image domains. Two approaches to this goal are addressed. One is a sparse-weight classification scheme, which works for images with different intensities, aiming to use the discriminative features of the images against the discriminative ones. The other, a non-sparsity-weight classification scheme, which is based on a fixed and a non-variable number of images. The proposed method is also validated online with real-data data from the online classification task.


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