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原文連結
論文資訊
- 類型：已發表論文
- 日期：2009
摘要
Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the 社會 網絡 of a 族群. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual's behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day's remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dime
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