Adaptive Canonical Correlation Analysis for Time-Series Prediction and Learning


Adaptive Canonical Correlation Analysis for Time-Series Prediction and Learning – We propose a new nonparametric model for classification of time series, based on similarity between the temporal features. Specifically, we take the form of a novel family of time series, namely, linear time series, which are characterized by features of the observations in time. The proposed model is based on two novel contributions. First, we use the data to determine the model’s features, and infer their dependence within a particular domain. Second, a set of prior distributions for each data vector are extracted, and the data is used to infer the model’s features. This approach is an extension of the traditional approach of learning to learn, to discover the best possible predictor from data. We report results on the use of the proposed model to learn from time series.

Deep-learning methods have been successfully applied to the design of medical domain applications and to medical imaging. However, deep models, such as deep neural networks (DNNs), do not exhibit robustness when applied to medical data. In this paper, we propose a hybrid, deep-learning-centric, efficient and scalable deep-learning method to enhance the performance of DNNs and other deep-learning-based approaches. The proposed method aims to improve the performance of DNNs by enhancing some discriminative representations of the data using deep learning. The proposed method is tested in three different medical domain applications, the first in an online MRI data set for the purpose of validation. The performance improvements are achieved with different DNN models, for which DNNs are not available and for which deep models are not implemented. In this paper, we perform a systematic empirical evaluation of our DNN-based deep-learning method for improving the performance of DNN-based deep vision approaches. The results indicate that the proposed method is competitive in terms of its effectiveness and efficiency.

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Adaptive Canonical Correlation Analysis for Time-Series Prediction and Learning

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  • Protein Secondary Structure Prediction using Stochastic Blockmodels in Protein Structure Measurements

    3D Scanning Network for Segmentation of Medical ImagesDeep-learning methods have been successfully applied to the design of medical domain applications and to medical imaging. However, deep models, such as deep neural networks (DNNs), do not exhibit robustness when applied to medical data. In this paper, we propose a hybrid, deep-learning-centric, efficient and scalable deep-learning method to enhance the performance of DNNs and other deep-learning-based approaches. The proposed method aims to improve the performance of DNNs by enhancing some discriminative representations of the data using deep learning. The proposed method is tested in three different medical domain applications, the first in an online MRI data set for the purpose of validation. The performance improvements are achieved with different DNN models, for which DNNs are not available and for which deep models are not implemented. In this paper, we perform a systematic empirical evaluation of our DNN-based deep-learning method for improving the performance of DNN-based deep vision approaches. The results indicate that the proposed method is competitive in terms of its effectiveness and efficiency.


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