Return to search

Simultaneous Variable and Feature Group Selection in Heterogeneous Learning: Optimization and Applications

abstract: Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous data, it is interesting to design efficient machine learning models that are capable of performing variable selection and feature group (data source) selection simultaneously (a.k.a bi-level selection). In this thesis, I carry out research along this direction with a particular focus on designing efficient optimization algorithms. I start with a unified bi-level learning model that contains several existing feature selection models as special cases. Then the proposed model is further extended to tackle the block-wise missing data, one of the major challenges in the diagnosis of Alzheimer's Disease (AD). Moreover, I propose a novel interpretable sparse group feature selection model that greatly facilitates the procedure of parameter tuning and model selection. Last but not least, I show that by solving the sparse group hard thresholding problem directly, the sparse group feature selection model can be further improved in terms of both algorithmic complexity and efficiency. Promising results are demonstrated in the extensive evaluation on multiple real-world data sets. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

Identiferoai:union.ndltd.org:asu.edu/item:26854
Date January 2014
ContributorsXiang, Shuo (Author), Ye, Jieping (Advisor), Mittelmann, Hans D (Committee member), Davulcu, Hasan (Committee member), He, Jingrui (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeDoctoral Dissertation
Format120 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

Page generated in 0.0018 seconds