Sparse Clustering via Convex Optimization

Sparse Clustering via Convex Optimization – We propose a new algorithm named Fast Caffe to solve sparse clustering problems. It is based on the observation that if the data points in a dataset are sparse at some point in time, then, our algorithm can learn the same sparse clustering problem as an ordinary Caffe. This is a crucial criterion for any Caffe with sparse data, even when using non-convex regularization. Our experiments on real data show that our algorithm significantly outperforms the normal Caffe in terms of clustering performance, clustering difficulty, and computation time.

We present a nonlinear model to model the temporal evolution of human knowledge about the world. Our approach is to first embed temporally related knowledge into the form of a multidimensional variable. We then embed the inter- and intra-variable covariate into a multidimensional structure in order to model the temporal motion in the multi-dimensional space. The multidimensional structure serves as a feature representation of multidimensional variables and represents temporally related variables in such a way that temporal evolution is also modeled as a multidimensional process of continuous evolution. The multidimensional structure is computed through a novel approach of learning from multidimensional features in a set of labeled items by using a multi-layer recurrent neural network. Experiments on large-scale public datasets show that we achieve state-of-the-art performance on real-world datasets.

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Sparse Clustering via Convex Optimization

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  • Nonlinear Models in Probabilistic Topic Models

    Towards a better understanding of the intrinsic value of training topic modelsWe present a nonlinear model to model the temporal evolution of human knowledge about the world. Our approach is to first embed temporally related knowledge into the form of a multidimensional variable. We then embed the inter- and intra-variable covariate into a multidimensional structure in order to model the temporal motion in the multi-dimensional space. The multidimensional structure serves as a feature representation of multidimensional variables and represents temporally related variables in such a way that temporal evolution is also modeled as a multidimensional process of continuous evolution. The multidimensional structure is computed through a novel approach of learning from multidimensional features in a set of labeled items by using a multi-layer recurrent neural network. Experiments on large-scale public datasets show that we achieve state-of-the-art performance on real-world datasets.


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