Scalable Algorithms for Learning Low-rank Mixtures with Large-Margin Classification


Scalable Algorithms for Learning Low-rank Mixtures with Large-Margin Classification – This paper presents a methodology for a hierarchical clustering model for classification tasks that use two or more classes. The class-specific clustering model is based in hierarchical clustering and can also be used to predict the clustering probability. The model can be used for all scenarios in which it is more suitable as a tool for clustering data.

Deep neural networks have attracted a lot of attention for their ability to efficiently learn nonlinear representations of images. However, they can also be used to learn representations of complex image functions due to their high computational complexity. Learning representation of complex images in deep networks requires many computations. We propose a method to learn a simple, generic image representation directly from images. The method is based on the use of convolutional layers to learn a deep representation of complex image functions. The learned representation represents the underlying information such as the pose, viewpoint and illumination. We demonstrate this method on several images with varying pose, illumination and viewpoint variation: a car, a man, and more importantly, a pedestrian. We achieve state-of-the-art reconstruction accuracies on MNIST, CIFAR10 and ImageNet datasets, but significantly outperforms the state-of-the-art on CIFAR-10, CIFAR100, and MS-B500 datasets. We evaluate the method on three challenging datasets. Experimental results show that our method is able to accurately reconstruct images from complex image functions such as poses, viewpoint and illumination.

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Scalable Algorithms for Learning Low-rank Mixtures with Large-Margin Classification

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  • Adaptive Sparse Convolutional Features For Deep Neural Network-based Audio Classification

    The Power of Over-parametrization for Training Deep Convolutional Neural NetworksDeep neural networks have attracted a lot of attention for their ability to efficiently learn nonlinear representations of images. However, they can also be used to learn representations of complex image functions due to their high computational complexity. Learning representation of complex images in deep networks requires many computations. We propose a method to learn a simple, generic image representation directly from images. The method is based on the use of convolutional layers to learn a deep representation of complex image functions. The learned representation represents the underlying information such as the pose, viewpoint and illumination. We demonstrate this method on several images with varying pose, illumination and viewpoint variation: a car, a man, and more importantly, a pedestrian. We achieve state-of-the-art reconstruction accuracies on MNIST, CIFAR10 and ImageNet datasets, but significantly outperforms the state-of-the-art on CIFAR-10, CIFAR100, and MS-B500 datasets. We evaluate the method on three challenging datasets. Experimental results show that our method is able to accurately reconstruct images from complex image functions such as poses, viewpoint and illumination.


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