An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
Mathematical Programming: Series A and B
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
The Journal of Machine Learning Research
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Computational Statistics & Data Analysis
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
TIARA: a visual exploratory text analytic system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
SystemT: an algebraic approach to declarative information extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
A framework for summarizing and analyzing twitter feeds
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGMOD Record
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Social media platforms such as blogs, Twitter® accounts, and online discussion sites are large-scale forums where every individual can potentially voice an influential public opinion. According to recent surveys, a massive number of Internet users are turning to such forums to collect recommendations and reviews for products and services, and to shape their individual choices and stances by the commentary of the online community as a whole. The unsupervised extraction of insight from unstructured user-generated web content requires new methodologies that are likely to be rooted in natural language processing and machine-learning techniques. Furthermore, the unprecedented scale of data begging to be analyzed necessitates the implementation of these methodologies on modern distributed computing platforms. In this paper, we describe a flexible new family of low-rank matrix approximation algorithms for modeling topics in a given corpus of documents (e.g., blog posts and tweets). We benchmark distributed optimization algorithms for running these models in a Hadoopi-enabled cluster environment. We describe online learning strategies for tracking the evolution of ongoing topics and rapidly detecting the emergence of new themes in a streaming setting.