High Dimensional Feature Selection Methods for Sparse Classifiers


High Dimensional Feature Selection Methods for Sparse Classifiers – This paper studies the use of latent Dirichlet allocation (LDA) in the classification task of image segmentation from a single dataset. The purpose of our work is to leverage the ability of lDA to obtain discriminative features from the source dataset. A lDA can be viewed as a generic representation of unlabeled data which allows for use of feature selection techniques. We demonstrate that its high performance can be achieved by using a simple non-parametric, but effective method for sparse classification. The proposed method is able to efficiently recover the feature from training data and by leveraging the unlabeled dataset with a simple non-parametric representation. We evaluate our method on image segmentation datasets, and compare it to state-of-the-art LDA-based methods on two datasets.

Learning to predict future events is challenging because of the large, complex, and unpredictable nature of the data. Despite the enormous volume of available data, supervised learning has made great progress in recent years in learning to predict the future rather than in predicting the past. In this paper, we present a framework for modeling and predicting the future of data by non-Gaussian prior approximating latent Gaussian processes. The underlying assumptions are to be established in the context of non-Gaussian prior approximating learning, and we further elaborate on these assumptions in a neural-network architecture. We evaluate this network on two datasets: the Long Short-Term Memory and the Stanford Attention Framework dataset, where we show that the model achieves state-of-the-art performance with good accuracy.

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High Dimensional Feature Selection Methods for Sparse Classifiers

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  • Stochastic Dual Coordinate Optimization with Side Information

    Hierarchical Gaussian Process ModelsLearning to predict future events is challenging because of the large, complex, and unpredictable nature of the data. Despite the enormous volume of available data, supervised learning has made great progress in recent years in learning to predict the future rather than in predicting the past. In this paper, we present a framework for modeling and predicting the future of data by non-Gaussian prior approximating latent Gaussian processes. The underlying assumptions are to be established in the context of non-Gaussian prior approximating learning, and we further elaborate on these assumptions in a neural-network architecture. We evaluate this network on two datasets: the Long Short-Term Memory and the Stanford Attention Framework dataset, where we show that the model achieves state-of-the-art performance with good accuracy.


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