Deep Structured Prediction for Low-Rank Subspace Recovery


Deep Structured Prediction for Low-Rank Subspace Recovery – Deep learning provides a general framework for automatically discovering feature representations from a large-scale dataset. This paper uses a deep neural network to learn feature representations from the raw image with a single feed-forward network. Specifically, the network is trained on a training set of images and a prediction set of feature representations extracted from the training set. As the network trains, its feature representations are learned for the training data. We show that even trained neural networks can learn such representations. In particular, we show that the trained model has good predictive power when the data is sufficiently large without relying on hand-crafted features. We also show empirically that the trained network performs better than the trained model when it is given a prediction model in the training set. In addition, a test dataset and a benchmark set are used to demonstrate the superiority of our approach over the trained model.

It is common that solving problems with nonnegative matrix factors with finite sample size are extremely difficult and time-consuming for non-convex systems. However, as matrix factorization is an unsupervised learning algorithm, it is far from being standard for the supervised problem. In this paper, we propose a novel approach for solving nonnegative matrix factorization problems using unsupervised learning where the nonnegative matrix factors are generated by solving an unsupervised optimization problem. We demonstrate that this approach is very suitable for the problem of learning nonnegative matrix factorization under a nonparametric framework. We also demonstrate its effectiveness on the data acquisition problem, showing that the approach can be used to obtain very close to state-of-the-art results in the literature.

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Deep Structured Prediction for Low-Rank Subspace Recovery

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    Robust Nonnegative Matrix Factorization via Non-convex Matrix RegularizationIt is common that solving problems with nonnegative matrix factors with finite sample size are extremely difficult and time-consuming for non-convex systems. However, as matrix factorization is an unsupervised learning algorithm, it is far from being standard for the supervised problem. In this paper, we propose a novel approach for solving nonnegative matrix factorization problems using unsupervised learning where the nonnegative matrix factors are generated by solving an unsupervised optimization problem. We demonstrate that this approach is very suitable for the problem of learning nonnegative matrix factorization under a nonparametric framework. We also demonstrate its effectiveness on the data acquisition problem, showing that the approach can be used to obtain very close to state-of-the-art results in the literature.


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