Feature Selection from Unstructured Text Data Using Unsupervised Deep Learning – Automatic diagnosis of Alzheimer’s disease (AD) and dementia remains a challenging problem due to large variation in the clinical and disease-specific data. In order to address this problem, large-scale datasets of neuroimaging data are required to deal with large-scale multi-label data. In this paper we focus on the task of image classification which is to identify the best images for a given task, when the training data are different. An efficient and tractable algorithm was developed to classify a task. This algorithm works on a class of images, and is applied to the classification task to avoid overfitting. The algorithm is evaluated using both simulated and real-world images taken from the same dataset. It is found to provide strong performance in classification tasks when used as an input for training the model. In an open-source MATLAB-based system we built a large dataset of real images. This dataset contains more than 70,000 images of different classifiers. We tested the proposed algorithm on several benchmark datasets. We find that the proposed approach outperforms existing unsupervised methods by a large margin on the most challenging data.

The work on unsupervised kernel classification relies on the problem of segmentation from a set of images from a high-dimensional metric. The purpose of this approach is to predict the parameters of the feature class, while minimizing the classification error. Our idea is to jointly estimate the metric and the classification error. This is achieved by jointly sampling the input and labels along the training set, which we refer to as the test set. In recent work, we have proposed a semi-supervised learning based method to learn the class labels. This method learns the metric on the test set, and the labels of the test set, respectively. We demonstrate the efficiency of our approach on several publicly available datasets, including LFW (the largest dataset for supervised classification), and on the MNIST dataset (the largest dataset for unlabeled data). The proposed method outperforms recent state-of-the-art unsupervised features-based methods.

Cortical activations and novelty-promoting effects in reward-based learning

Fast Non-convex Optimization with Strong Convergence Guarantees

Feature Selection from Unstructured Text Data Using Unsupervised Deep Learning

The Data Science Approach to Empirical Risk Minimization

High-Dimensional Feature Selection Through Kernel Class ImputationThe work on unsupervised kernel classification relies on the problem of segmentation from a set of images from a high-dimensional metric. The purpose of this approach is to predict the parameters of the feature class, while minimizing the classification error. Our idea is to jointly estimate the metric and the classification error. This is achieved by jointly sampling the input and labels along the training set, which we refer to as the test set. In recent work, we have proposed a semi-supervised learning based method to learn the class labels. This method learns the metric on the test set, and the labels of the test set, respectively. We demonstrate the efficiency of our approach on several publicly available datasets, including LFW (the largest dataset for supervised classification), and on the MNIST dataset (the largest dataset for unlabeled data). The proposed method outperforms recent state-of-the-art unsupervised features-based methods.