Developing practical automatic metadata assignment and evaluation tools for internet resources
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Computationally efficient scoring of activity using demographics and connectivity of entities
Information Technology and Management
Robust weighted kernel logistic regression in imbalanced and rare events data
Computational Statistics & Data Analysis
Scalable subspace logistic regression models for high dimensional data
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
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The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining and high-dimensional classification problems. LR is well-understood and widely used in the statistics, machine learning, and data analysis communities. Its benefits include a firm statistical foundation and a probabilistic model useful for “explaining” the data. There is a perception that LR is slow, unstable, and unsuitable for large learning or classification tasks. Through fast approximate numerical methods, regularization to avoid numerical instability, and an efficient implementation we will show that LR can outperform modern algorithms like Support Vector Machines (SVM) on a variety of learning tasks. Our novel implementation, which uses a modified iteratively re-weighted least squares estimation procedure, can compute model parameters for sparse binary datasets with hundreds of thousands of rows and attributes, and millions or tens of millions of nonzero elements in just a few seconds. Our implementation also handles real-valued dense datasets of similar size.