A Fast Convex Relaxation for Efficient Sparse Subspace Clustering


A Fast Convex Relaxation for Efficient Sparse Subspace Clustering – While deep neural networks have made impressive progress in many computer vision applications, they are still suffering from its limitations in particular when the training data is sparse. In this paper, we propose to tackle these limitations by using a convolutional neural network (CNN) to train a CNN for a single sparse subspace clustering problem. Our first model is a convolutional neural network with a convolutional convolutional layer. The CNN is trained with two layers of LSTMs and each layer is used to learn a convolutional convolutional sparse subspace. By combining the learned sparse subspaces, the CNN is trained to learn the corresponding sparse subspace using the training set. Through extensive numerical experiments, we demonstrate the effectiveness of our CNN for solving the sparse subspace clustering problem.

We present a model in which the content of a blog is extracted from its metadata using data mining techniques. Our approach is based on three steps: (1) a query-based approach for extracting content from metadata of articles, (2) a question-based approach for extracting content from metadata of articles and answering them; (3) a graph-based approach for extracting content from a graph; (when query-based analysis is feasible, in this case this is the first step in the graph). The graph is a hierarchical graph with content in a collection of nodes, each containing one or more words. We apply this approach to query-based and question-based content extraction from a blog metadata database, and also to the word embeddings of posts. Our approach is based on a semantic similarity measure using a hierarchical structure, and utilizes a novel learning-to-learning algorithm for clustering data collected through query-based analysis. We show that our approach performs well (by a large margin) on both datasets. We also show that our approach outperforms a state-of-the-art approach.

CNN based Multi-task Learning through Transfer

Learning and Inference with Predictive Models from Continuous Data

A Fast Convex Relaxation for Efficient Sparse Subspace Clustering

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  • Clustering and Classification with Densely Connected Recurrent Neural Networks

    Web-based Media Retrieval: An Evaluation Network for Reviews and BlogsWe present a model in which the content of a blog is extracted from its metadata using data mining techniques. Our approach is based on three steps: (1) a query-based approach for extracting content from metadata of articles, (2) a question-based approach for extracting content from metadata of articles and answering them; (3) a graph-based approach for extracting content from a graph; (when query-based analysis is feasible, in this case this is the first step in the graph). The graph is a hierarchical graph with content in a collection of nodes, each containing one or more words. We apply this approach to query-based and question-based content extraction from a blog metadata database, and also to the word embeddings of posts. Our approach is based on a semantic similarity measure using a hierarchical structure, and utilizes a novel learning-to-learning algorithm for clustering data collected through query-based analysis. We show that our approach performs well (by a large margin) on both datasets. We also show that our approach outperforms a state-of-the-art approach.


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