Learning sparse graphical models for data restoration and multi-label classification
Qiang Li · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · 2017
Sparse probabilistic graphical models play an important role in structured prediction when the dependency structure is unknown.By inducing sparsity over edge parameters, a typical sparse graphical model can combine structure learning and parameter estimation under a unified optimization framework.In this thesis, we propose three specific sparse graphical models accompanied by their applications in data restoration and multi-label classification respectively.For the data restoration task, we propose random mixed field (RMF) model to explore mixed-attribute correlations among data.The RMF model is capable of handling mixed-attribute data denoising and imputation simultaneously.Meanwhile, RMF employs a structured meanfield variational approach to decouple continuous-discrete interactions to achieve approximate inference.The effectiveness of this model is evaluated on both synthetic and real-world data.For the multi-label classification task, we propose correlated logistic model (CorrLog) and conditional graphical lasso (CGL), to learn conditional label correlations.(1) The CorrLog model characterizes pairwise label correlations via scalar parameters, thus effects in an explicit (or direct) fashion.More specifically, CorrLog extends conventional logistic regression by jointly modelling label correlations.In addition, elastic-net regularization is employed to induce sparsity over the scalar parameters that define label correlations.CorrLog can be efficiently learned by regularized maximum pseudo likelihood estimation which I feel so grateful to have Prof. Dacheng Tao and Prof. Jane You as my supervisors in UTS and PolyU respectively.I have learned a lot from their immense and extensive knowledge, meticulous attitude to