Robust Subspace Modeling with Multi-view Feature Space Representation


Robust Subspace Modeling with Multi-view Feature Space Representation – Multi-view segmentation (MVS) is a recent, yet promising model for multiple image classification tasks. In this work, we propose a novel Multilayer Perceptron-LSTM (MLP-LSTM) architecture to train two MLP networks in each view. Compared to existing neural networks trained on a single view using different weights, MLP networks can be directly trained and evaluated using different models, allowing each learning component to receive a similar amount of attention. We develop a novel deep learning technique for learning MLP networks to predict the expected semantic representation of a single feature space. This allows us to provide a new learning objective for multi-view segmentation, which can significantly boost the performance of this segmentation model. An extensive study on three real datasets from the web show that our proposed network models achieve competitive accuracy on all real datasets, outperforming all existing methods.

In this work, we show how to model time-dependent random variables in a stochastic Bayesian network and how they impact the stochastic gradient descent. First, we propose an auxiliary function that can be used to directly measure the relative gradient error. Secondly, we extend the supervised decision block to a multi-level supervised learning model, where the posterior of the stochastic block is updated in the process of learning the stochastic gradient. Our approach addresses two key challenges in stochastic Bayesian networks: 1) stochastic gradient descent and 2) time-observable learning and learning over complex data and complex data. We show how to update the posterior in a supervised manner using the stochastic method as the auxiliary function. Experimental results show that the proposed method significantly improves the state of the art supervised stochastic Bayesian network prediction performance by an incremental number of orders of magnitude over a standard variational regularization-based stochastic gradient descent model.

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Robust Subspace Modeling with Multi-view Feature Space Representation

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  • Convolutional Neural Network Based Parsing of Large Vocabulary Neuroimage Data

    Diving into the unknown: Fast and accurate low-rank regularized stochastic variational inferenceIn this work, we show how to model time-dependent random variables in a stochastic Bayesian network and how they impact the stochastic gradient descent. First, we propose an auxiliary function that can be used to directly measure the relative gradient error. Secondly, we extend the supervised decision block to a multi-level supervised learning model, where the posterior of the stochastic block is updated in the process of learning the stochastic gradient. Our approach addresses two key challenges in stochastic Bayesian networks: 1) stochastic gradient descent and 2) time-observable learning and learning over complex data and complex data. We show how to update the posterior in a supervised manner using the stochastic method as the auxiliary function. Experimental results show that the proposed method significantly improves the state of the art supervised stochastic Bayesian network prediction performance by an incremental number of orders of magnitude over a standard variational regularization-based stochastic gradient descent model.


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