Luwen Huangfu

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In a biometric recognition task, the manifold of data is the result of the interactions between the sub-manifold of dynamic factors of subjects and the sub-manifold of static factors of subjects. Therefore, instead of directly constructing the graph Laplacian of samples, we firstly divide each subject data into a static part (subject-invariant part) and a(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)
Sparse Representation (SR) shows powerful discriminating power when the training samples are sufficient to construct an overcomplete 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)
Extracting emotions from online reviews is crucial to many security-related applications as well as applications in other domains. Traditional approaches to emotion extraction have mainly focused on mining the polarities of opinions or using annotated data to extract emotion types. Emotion theories, which identify the underlying cognitive structure and(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)
Opinion mining has gained increasing attention and shown great practical value in recent years. Existing research on opinion mining mainly focuses on the extraction of lexicon orientation and opinion targets. The explanations of opinions, which are potentially valuable for many applications, are totally ignored. To address this specific research challenge,(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)
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)