Combining Disparate Information for Machine Learning
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This dissertation considers information fusion for four different
types of machine learning problems: anomaly detection, information retrieval, collaborative filtering and structure learning for time series, and focuses on a common theme — the benefit to combining disparate information resulting in improved algorithm performance.
In this dissertation, several new algorithms and applications to
real-world datasets are presented. First, a novel approach called
Pareto Depth Analysis (PDA) is proposed for combining different
dissimilarity metrics for anomaly detection. PDA is applied to
video-based anomaly detection of pedestrian trajectories. Next,
following a similar idea, we propose to use a similar Pareto Front
method for a multiple-query information retrieval problem when
different queries represent different semantic concepts. Pareto Front information retrieval is applied to multiple query image retrieval. Then, we extend a recently proposed collaborative retrieval approach to incorporate complementary social network information, an approach we call Social Collaborative Retrieval (SCR). SCR is applied to a music recommendation system that combines both user history and friendship network information to improve recall and weighted recall performance. Finally, we propose a framework that combines time series data at different time scales and offsets for more accurate estimation of multiple precision matrices. We propose a general fused graphical
lasso approach to jointly estimate these precision matrices. The
framework is applied to modeling financial time series data.