Feature regularization and learning for human activity recognition.
Date
2018
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Abstract
Feature extraction is an essential component in the design of human activity
recognition model. However, relying on extracted features alone for learning often makes the model a suboptimal model. Therefore, this research
work seeks to address such potential problem by investigating feature regularization. Feature regularization is used for encapsulating discriminative
patterns that are needed for better and efficient model learning. Firstly, a
within-class subspace regularization approach is proposed for eigenfeatures
extraction and regularization in human activity recognition. In this ap-
proach, the within-class subspace is modelled using more eigenvalues from
the reliable subspace to obtain a four-parameter modelling scheme. This
model enables a better and true estimation of the eigenvalues that are distorted by the small sample size effect. This regularization is done in one
piece, thereby avoiding undue complexity of modelling eigenspectrum differently. The whole eigenspace is used for performance evaluation because
feature extraction and dimensionality reduction are done at a later stage
of the evaluation process. Results show that the proposed approach has
better discriminative capacity than several other subspace approaches for
human activity recognition. Secondly, with the use of likelihood prior probability, a new regularization scheme that improves the loss function of deep
convolutional neural network is proposed. The results obtained from this
work demonstrate that a well regularized feature yields better class discrimination in human activity recognition. The major contribution of the
thesis is the development of feature extraction strategies for determining
discriminative patterns needed for efficient model learning.
Description
Doctoral Degree. University of KwaZulu-Natal, Durban.