Efficient Sparse Subspace Clustering via Matrix Completion – While Convolutional neural networks (CNNs) have become the most explored and powerful tool for supervised learning on image data, little attention has been focused on the learning of sparse representations. In this paper, we investigate sparse representation learning and learn sparse representations from high-dimensional data, using the deep CNN family. We exploit the fact that the embedding space of a CNN representation can only contain sparse information, and not the underlying image representation. We propose an efficient method to learn sparse representations in CNNs using a deep CNN architecture. We study the nonlinearity of the embedding space and the problem of learning sparse representations in CNNs. We derive a novel deep learning method that significantly improves the performance when compared to conventional CNN-based approaches.
Most medical applications require automated clinical diagnosis. In this work, we show how clinical applications can generate customized diagnosis models based on medical data. Our model is based on the concept of personalized data, which is a fundamental part of clinical applications. We show that such a machine learning model can, by learning the human patient characteristics, learn diagnoses from data that are relevant to the patients’ condition. We further show how these medical diagnoses could be extracted by a machine learning model which uses the patient characteristics of the patients as well as the patient characteristics of the patients. The model has the ability to adapt the patient characteristics to the data, using a specific patient description for patients and the classification of the patients’ status using the human patients. This model can also be used to automatically process the patient characteristics as a whole instead of just their diagnosis.
Sparse Depth Estimation via Sparse Gaussian Mixture Modeling and Constraint Optimization
T-distributed multi-objective regression with stochastic support vector machines
Efficient Sparse Subspace Clustering via Matrix Completion
Learning to predict footballs using deep learning
Towards the Collaborative Training of Automated Cardiac Diagnosis ModelsMost medical applications require automated clinical diagnosis. In this work, we show how clinical applications can generate customized diagnosis models based on medical data. Our model is based on the concept of personalized data, which is a fundamental part of clinical applications. We show that such a machine learning model can, by learning the human patient characteristics, learn diagnoses from data that are relevant to the patients’ condition. We further show how these medical diagnoses could be extracted by a machine learning model which uses the patient characteristics of the patients as well as the patient characteristics of the patients. The model has the ability to adapt the patient characteristics to the data, using a specific patient description for patients and the classification of the patients’ status using the human patients. This model can also be used to automatically process the patient characteristics as a whole instead of just their diagnosis.