Learning Deep Transform Architectures using Label Class Discriminant Analysis


Learning Deep Transform Architectures using Label Class Discriminant Analysis – We propose and analyze a framework for automatic segmentation of high-resolution face images by exploiting the temporal and spatial information. Our novel framework is formulated as an extension of the K-SVD method and its predecessors. It consists of a Convolutional Neural Network (CNN), a Convolutional Linear Network (CNNLN), a Convolutional Neural Network (CNN-DNN), Deep Convolutional Neural Network (CNN-DNN), and a Convolutional Neural Network (CNN-RNN). We demonstrate its ability to extract high-resolution face images and segment large-scale images while minimizing the task cost with a small training set size. The CNN is trained end-to-end. Our experimental results show that our approach outperforms the state-of-the-art approaches in terms of segmentation cost while obtaining lower annotations.

Deep learning has been widely used to discover, understand and manage complex patterns in data. While recent experiments on deep learning systems based on deep neural networks have shown great success in learning and predicting heart beats, the underlying machine learning paradigm of learning from data is still largely unexplored. Recent studies have shown the potential of deep neural networks as a promising technology to produce machine learning models which produce accurate, robust and scalable data that can be applied to other data-driven applications, such as the medical workflow.

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Learning Deep Transform Architectures using Label Class Discriminant Analysis

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  • Sparse Feature Analysis and Feature Separation for high-dimensional sequential data

    A new Stochastic Unsupervised Approach to Patient-Specific Heartbeat PredictionDeep learning has been widely used to discover, understand and manage complex patterns in data. While recent experiments on deep learning systems based on deep neural networks have shown great success in learning and predicting heart beats, the underlying machine learning paradigm of learning from data is still largely unexplored. Recent studies have shown the potential of deep neural networks as a promising technology to produce machine learning models which produce accurate, robust and scalable data that can be applied to other data-driven applications, such as the medical workflow.


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