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Sequential Modeling of Topic Dynamics with Multiple Timescales
Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, Naonori Ueda
Article No.: 19
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while...
Discriminative Topic Modeling Based on Manifold Learning
Seungil Huh, Stephen E. Fienberg
Article No.: 20
Topic modeling has become a popular method used for data analysis in various domains including text documents. Previous topic model approaches, such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA), have shown...
Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions...
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
Jianhui Chen, Ji Liu, Jieping Ye
Article No.: 22
We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multitask learning formulation, in which the sparse and low-rank patterns are induced by a cardinality...
Large Linear Classification When Data Cannot Fit in Memory
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin
Article No.: 23
Recent advances in linear classification have shown that for applications such as document classification, the training process can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be...
Connecting Two (or Less) Dots: Discovering Structure in News Articles
Dafna Shahaf, Carlos Guestrin
Article No.: 24
Finding information is becoming a major part of our daily life. Entire sectors, from Web users to scientists and intelligence analysts, are increasingly struggling to keep up with the larger and larger amounts of content published every day. With...