Learning Feature Hierarchies via Regression Trees


Learning Feature Hierarchies via Regression Trees – As the quality of information grows, so too does the need for a reliable way to classify. In this paper, we propose a novel method to perform classification in the form of a probabilistic model that estimates a latent covariance matrix from multiple input features. By this means, the model is an implicit model that we can use to automatically classify nonlinear structures while being flexible to accommodate complex inputs. Specifically, we propose a probabilistic inference algorithm that automatically estimates the latent covariance matrix as the output of a robust estimator. Experimental results demonstrate the effectiveness of our model in tasks with a variety of input features ranging from visual and motor scenes, to biomedical applications.

With the proliferation of digital art, there have been numerous applications of unsupervised sparse learning to automatically estimate an object from a sparse representation using a deep convolutional network. We propose an unsupervised sparse estimation framework based on an iterative process of minimizing and discretizing the input data. Our algorithm achieves a fast reconstruction using linear convergence rates, a lower memory footprint, and higher accuracy than many state-of-the-art unsupervised sparse detection algorithms. We also show that the residuals of the object can be extracted by the encoder as a regularity function which is very useful for unsupervised learning. We then extend that sparse reconstruction procedure to an unsupervised setting where the reconstruction can take place offline. Further, we show that sparse reconstruction can lead to better performance in image classification, i.e. object detection and classification.

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Learning Feature Hierarchies via Regression Trees

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  • Adversarially Learned Online Learning

    Dictionary Learning, Super-Resolution and Texture Matching with Hashing AlgorithmWith the proliferation of digital art, there have been numerous applications of unsupervised sparse learning to automatically estimate an object from a sparse representation using a deep convolutional network. We propose an unsupervised sparse estimation framework based on an iterative process of minimizing and discretizing the input data. Our algorithm achieves a fast reconstruction using linear convergence rates, a lower memory footprint, and higher accuracy than many state-of-the-art unsupervised sparse detection algorithms. We also show that the residuals of the object can be extracted by the encoder as a regularity function which is very useful for unsupervised learning. We then extend that sparse reconstruction procedure to an unsupervised setting where the reconstruction can take place offline. Further, we show that sparse reconstruction can lead to better performance in image classification, i.e. object detection and classification.


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