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Finding robust transfer features for unsupervised domain adaptation

Gao Depeng, Wu Rui, Liu Jiafeng, Fan Xiaopeng, Tang Xianglong

International Journal of Applied Mathematics and Computer Science

2020

Vol. 30, no. 1

99--112

An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.

Application of clustering techniques for visually evoked potentials based detection of vision impairments

Vijean V., M H., Yaacob S., Sulaiman M. N. B.

Biocybernetics and Biomedical Engineering

2014

Vol. 34, no. 3

169--177

Visually evoked potentials (VEP) are evoked responses of the brain corresponding to a specific visual stimulus. Ophthalmologists often refer their patients to VEP test if the latter suffers any vision abnormalities that cannot be diagnosed using conventional analysis. By investigating the VEP responses, medical experts can narrow down the possible cause of the defect. Although this method provides valuable information to the medical practitioner, there are several drawbacks of the analysis that can affect the diagnosis result. The conventional averaging of the signals results in inter-trial variation between the VEP responses to be lost. This method also requires large number of trials, which causes fatigue in patients and reduces the diagnostic accuracy. Therefore, we have proposed a new method of analysis using statistical features derived from time and spectral space for the discrimination of vision impairments. Feature enhancement methods such as feature weighting and dimensional reduction are used to enhance the statistical features prior to the analysis. Four clustering methods are employed to increase the interclass separability of the control and myopic features while reducing the within class variability. The dimension of the weighted features is reduced using a combination of principal component analysis (PCA) and independent component analysis (ICA) techniques prior to classification. The proposed method is able to achieve 100% accuracy using extreme learning machine (ELM) and multi layer neural network (MLNN) classifiers.

Linear discriminant analysis as EEG features reduction technique for brain-computer interfaces

Rak R. J., Majkowski A., Kołodziej M.

Przegląd Elektrotechniczny

2012

R. 88, nr 3a

28-30

BCI systems analyze the EEG signal and translate patient intentions into simple commands. Signal processing methods are very important in such systems. Signal processing covers: preprocessing, feature extraction, feature selection and classification. In the article authors present the results of implementing linear discriminant analysis as a feature reduction technique for BCI systems.

Systemy BCI analizują sygnał EEG i tłumaczą intencje użytkownika na proste polecenia. Ważnym elementem systemów BCI jest przetwarzanie sygnału. Obejmuje ono: przetwarzanie wstępne, ekstrakcję cech, selekcję cech i klasyfikację. W artykule autorzy prezentują wyniki badań z zastosowaniem liniowej analizy dyskryminacyjnej jako narzędzia do redukcji cech.