Bayesian Multi-Domain Clustering using k-Means and Two-way Interaction


Bayesian Multi-Domain Clustering using k-Means and Two-way Interaction – Learning a joint distribution over the data-sets is a challenging problem with several solutions. In order to address the problem we propose a two-stage approach that maps the data to the distribution of the data using a conditional probability distribution. Our framework includes a convex relaxation of joint distributions, a conditional probability measure and a latent space regression. Our model is able to learn its parameters and learn the joint distributions simultaneously, thus making it a better model for large-scale data analysis and learning tasks.

We propose a novel method for classification tasks, by first finding a score of items that the target (or a subset of items) is interested in. This is a very challenging task, and our method is motivated by the following question: how to predict the target of an item? The goal of this work is to infer its value of a set of items and use that value to generate a ranking metric. We propose an algorithm that learns a rank-based value which serves as a baseline to improve classification accuracy. The method is applied to two challenging categories, namely, text classification and video analysis. Our experiments demonstrate the effectiveness of using the rank-based value to improve classification performance.

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Bayesian Multi-Domain Clustering using k-Means and Two-way Interaction

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  • Improving Attention-Based Video Summarization with Deep Learning

    Learning to Rank for Passive Perception in Unlabeled DataWe propose a novel method for classification tasks, by first finding a score of items that the target (or a subset of items) is interested in. This is a very challenging task, and our method is motivated by the following question: how to predict the target of an item? The goal of this work is to infer its value of a set of items and use that value to generate a ranking metric. We propose an algorithm that learns a rank-based value which serves as a baseline to improve classification accuracy. The method is applied to two challenging categories, namely, text classification and video analysis. Our experiments demonstrate the effectiveness of using the rank-based value to improve classification performance.


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