Structural Correspondence Analysis for Semi-supervised Learning


Structural Correspondence Analysis for Semi-supervised Learning – Most current methods treat a set of discrete observations (e.g., a model and a test) as a collection of observations. Such approaches typically assume that samples are modeled as discrete samples, which may not be the case. In this work we present a new approach for classification experiments based on Bayesian networks, where the classifier is a single distribution over observations. In addition, we present a generalization error measure that enables us to compare the resulting classifiers to a subset of the observed distributions. To the best of our knowledge, our contribution is the first one to analyze data in this manner, outperforming a state-of-the-art classification algorithm in this task.

Visual object recognition (VA) has attracted significant interest due to its vast range of applications. However, the proposed approach is based on using low rank embedding models to solve the visual representation problem. In this work, we propose a novel low rank embedding learning framework for VA by using variational inference (VLI) in the VLI space to automatically generate low rank embeddings for visual objects. We propose an online variational inference scheme for embedding the posterior of a convolutional neural network in the VLI space. The proposed approach is formulated as a convolutional neural network (CNN) for VA which learns to infer the vignetting probability score of the convolutional network. This is performed using a single CNN as input to the VLI network. We demonstrate that this approach outperformed the state-of-the-art methods for VA on the IJBVA benchmark.

Learning with Variational Inference and Stochastic Gradient MCMC

Bayesian Models for Topic Models

Structural Correspondence Analysis for Semi-supervised Learning

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  • An extended Stochastic Block model for learning Bayesian networks from incomplete data

    Predicting Video Characteristics with Generative Adversarial NetworksVisual object recognition (VA) has attracted significant interest due to its vast range of applications. However, the proposed approach is based on using low rank embedding models to solve the visual representation problem. In this work, we propose a novel low rank embedding learning framework for VA by using variational inference (VLI) in the VLI space to automatically generate low rank embeddings for visual objects. We propose an online variational inference scheme for embedding the posterior of a convolutional neural network in the VLI space. The proposed approach is formulated as a convolutional neural network (CNN) for VA which learns to infer the vignetting probability score of the convolutional network. This is performed using a single CNN as input to the VLI network. We demonstrate that this approach outperformed the state-of-the-art methods for VA on the IJBVA benchmark.


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