Luwen Huangfu

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Sparse Representation (SR) shows powerful discriminating power when the training samples are sufficient to construct an over-complete dictionary. However, in the lack of training samples case, the dictionary is too small to sparsely represent the test sample which restricts the classification performance of sparse representation. In order to address this(More)
We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant(More)
Automated text classification technologies have enabled researchers to amass enormous collections of personal narratives posted to English-language weblogs. In this paper, we explore analogous approaches to identify personal narratives in Chinese weblog posts as a precursor to the future empirical studies of cross-cultural differences in narrative(More)
Twitter and other social media data are utilized for a wide variety of applications such as marketing and stock market prediction. Each application and appropriate domain of social media text presents its own challenges and benefits. We discuss methods for detecting obesity, a risk factor for Type II Diabetes Mellitus (T2DM), from the language of food on(More)