Name: Fernando Kentaro Inaba
Type: PhD thesis
Publication date: 28/02/2018
Advisor:

Namesort descending Role
Evandro Ottoni Teatini Salles Advisor *

Examining board:

Namesort descending Role
André Carlos Ponce de Leon Ferreira de Carvalho External Examiner *
Evandro Ottoni Teatini Salles Advisor *
Klaus Fabian Coco External Examiner *
Thiago Oliveira dos Santos External Examiner *
Thomas Walter Rauber External Examiner *

Summary: Extreme Learning Machine (ELM) has recently increased popularity and has been successfully applied to a wide range of applications. Variants using regularization are now a common practice in the state of the art in ELM field. The most commonly used regularization is the `2 norm, which improves generalization but results in a dense network. Regularization based on the elastic net has also been proposed but mainly applied to regression and binary classification problems. In this thesis, it is proposed a generalization of regularized ELM (R-ELM) for multiclass classification and multitarget regression problems. The use of `2,1 and Frobenius norm provided an appropriate generalization. Consequently, it was possible to show that R-ELM and OR-ELM are a particular case of the methods proposed in this thesis, termed GR-ELM and GOR-ELM, respectively. Furthermore, another method proposed in this thesis is the DGR-ELM, which is analternative method of GR-ELM for dealing with data that are naturally distributed. The alternating direction method of multipliers (ADMM) is the algorithm used to solve the resulting optimization problems. Message Passing Interface (MPI) in a Single Program, Multiple Data (SPMD) programming style is chosen for implementing DGR-ELM. Extensive experiments are conducted to evaluate the proposed method. Our experiments show that GR-ELM, DGR-ELM, and GOR-ELM have similar training and testing performance when compared to R-ELM and OR-ELM, although usually faster testing time is obtained with our method due to the compactness of the resulting network.

Keywords: `2,1 norm. regularization. extreme learning machine. multiclass classification. multitarget regression. outlier robust. alternating direction method of multipliers.

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