Efficient Sparse Subspace Clustering via Matrix Completion


Efficient Sparse Subspace Clustering via Matrix Completion – While Convolutional neural networks (CNNs) have become the most explored and powerful tool for supervised learning on image data, little attention has been focused on the learning of sparse representations. In this paper, we investigate sparse representation learning and learn sparse representations from high-dimensional data, using the deep CNN family. We exploit the fact that the embedding space of a CNN representation can only contain sparse information, and not the underlying image representation. We propose an efficient method to learn sparse representations in CNNs using a deep CNN architecture. We study the nonlinearity of the embedding space and the problem of learning sparse representations in CNNs. We derive a novel deep learning method that significantly improves the performance when compared to conventional CNN-based approaches.

We show that a simple but useful method for learning a mixture graph from data (i.e., the mixture model) has the advantage of being linear in the model size. Such a method is not necessarily sufficient for most applications. For example, in many situations, a mixture model is not exactly representative of the data, but as a sparse representation of the data, and can often take a large number of observations to attain an equivalent representation.

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Efficient Sparse Subspace Clustering via Matrix Completion

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  • Coupled Itemset Mining with Mixture of Clusters

    A unified approach to learning multivariate linear models via random forestsWe show that a simple but useful method for learning a mixture graph from data (i.e., the mixture model) has the advantage of being linear in the model size. Such a method is not necessarily sufficient for most applications. For example, in many situations, a mixture model is not exactly representative of the data, but as a sparse representation of the data, and can often take a large number of observations to attain an equivalent representation.


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