Scalable Matrix Factorization for Novel Inference in Exponential Families


Scalable Matrix Factorization for Novel Inference in Exponential Families – We present a neural-network-based method for the identification of high-dimensional and sparse binary patterns in probabilistic systems. Our framework combines a recurrent neural network (RNN) and a Gaussian process (GP). The recurrent encoder is a generative model which is adapted to learning from binary data. We also exploit this recurrent encoder to represent binary patterns using latent space models, which is a common form of binary data in probabilistic systems. Experiments on synthetic data demonstrate that the proposed deep learning approaches show better performance than state-of-the-art approaches.

Spectral similarity is a key concept in various research and practice areas. In this paper we describe a new method for estimating spectral similarity between two spectra, the spectral similarity of an image and its associated spectral similarity across objects. The method is based upon the similarity of a given image between two spectra from a distance-sensitive optical stream, which combines a Gaussian and a sparse representation of two spectra. The resulting spectral similarity matrix is a low-rank matrix which combines a Gaussian and a sparse representation of objects with a high correlation to the input image. Since the spectral similarity of an image is more correlated with the spectral similarity of the object, the proposed method is also more accurate. In experiments on real-world data, the proposed method produces better results than standard methods in terms of accuracy, outperforming the state-of-the-art methods.

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Scalable Matrix Factorization for Novel Inference in Exponential Families

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  • An Efficient Algorithm for Online Convex Optimization with Nonconvex Regularization

    A Unified Framework for Spectral Unmixing and Spectral Clustering of High-Dimensional DataSpectral similarity is a key concept in various research and practice areas. In this paper we describe a new method for estimating spectral similarity between two spectra, the spectral similarity of an image and its associated spectral similarity across objects. The method is based upon the similarity of a given image between two spectra from a distance-sensitive optical stream, which combines a Gaussian and a sparse representation of two spectra. The resulting spectral similarity matrix is a low-rank matrix which combines a Gaussian and a sparse representation of objects with a high correlation to the input image. Since the spectral similarity of an image is more correlated with the spectral similarity of the object, the proposed method is also more accurate. In experiments on real-world data, the proposed method produces better results than standard methods in terms of accuracy, outperforming the state-of-the-art methods.


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