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We propose selective supervised Latent Dirichlet Allocation (ssLDA) to boost the prediction performance of the widely studied supervised probabilistic topic models. We introduce a Bernoulli distribution for each word in one given document to select this word as a strongly or weakly discriminative one with respect to its assigned topic. The Bernoulli distribution is parameterized by the discrimination...
Nowadays, the prevalence of sharing tourist photos to online communities has created an increasing demand for mining discriminative architecture aspects from historic landmarks. Some previous researches have demonstrated that topic models could discover discriminative features represented by meaningful visual-topics. However, they seldom exploited the indicative function of geo-tags and the hierarchy...
With the rapid growth of digital libraries, more data and smart services are involved. People come to recognize the importance of digital libraries and the convenience they might bring to the society. However, the cost of owning a digital library is quite high, and many institutions do not have the ability to run and maintain a digital library by themselves, especially for massive data and complex...
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