A Unified Algorithm for Fast Robust Subspace Clustering – Deep neural networks (DCNNs) have become a valuable tool for many applications, including image classification, computer vision and motion-sensing. In this work, we propose a framework based on the use of deep neural networks (DNNs) to solve the sparse matrix and high dimension problem in images. We have evaluated our method on 3D images and compared it to other state-of-the-art DCNNs, including the one which uses deep recurrent neural networks. Results demonstrated that deep recurrent neural networks could be a very effective method of solving the sparse matrix problem, outperforming state-of-the-art DNNs, on a range of datasets. Deep recurrent neural network (RANSAC) can also be used to solve the sparse matrix problem.

We consider a supervised learning problem that aims at predicting a label’s probability of being likely to be found at a given point in time, and thus learning a sequence of labels from a set of data. While many state-of-the-art performance metrics on prediction time series have been shown to be accurate (e.g., BLEU), the cost of prediction on such models has been generally considered to be non-convex. This makes unsupervised learning an appealing approach to learn from noisy labeling data. In this work we propose an algorithm for learning structured predictor data which is also suitable for supervised learning. We evaluate our algorithm using synthetic and real-data datasets of label prediction. We analyze the effectiveness of our algorithm by observing that when the label prediction data is noisy the algorithm does not perform well on the synthetic data but on real data. We further experiment over a real and synthetic dataset of annotated data (over 25,000 labels), which provides a significant improvement over the vanilla prediction algorithm.

Convolutional-Neural-Network for Image Analysis

Structural Similarities and Outlier Perturbations

# A Unified Algorithm for Fast Robust Subspace Clustering

Linking and Between Event Groups via Randomized Sparse Subspace

Learning from Noisy Label AnnotationsWe consider a supervised learning problem that aims at predicting a label’s probability of being likely to be found at a given point in time, and thus learning a sequence of labels from a set of data. While many state-of-the-art performance metrics on prediction time series have been shown to be accurate (e.g., BLEU), the cost of prediction on such models has been generally considered to be non-convex. This makes unsupervised learning an appealing approach to learn from noisy labeling data. In this work we propose an algorithm for learning structured predictor data which is also suitable for supervised learning. We evaluate our algorithm using synthetic and real-data datasets of label prediction. We analyze the effectiveness of our algorithm by observing that when the label prediction data is noisy the algorithm does not perform well on the synthetic data but on real data. We further experiment over a real and synthetic dataset of annotated data (over 25,000 labels), which provides a significant improvement over the vanilla prediction algorithm.