Robust SVM with adaptive graph learning

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan

“…As a consequence, the samples with large estimation errors are regarded as outliers and their influences are reduced. In the literature, a number of robust loss functions have been developed, including function, Cauchy function, and Geman–McClure estimator, and so on ( Hu, Zhu, Zhu, Gan, 2020 , Zhu, Gan, Lu, Li, Zhang, 2020 ). However, the robust loss function was not designed to explore the issue of the imbalance classification.…”

“…(3) employs the ℓ 0 -norm constraint for each class to output exactly predefined non-zero elements. On the contrary, self-paced learning uses the ℓ 1 -norm constraint for all samples or other robust loss functions ( Zhu, Zhu, Zheng, 2020 , Hu, Zhu, Zhu, Gan, 2020 ) to estimate the sample weight without guaranteeing the exact number of non-zero elements. As a result, compared to self-paced learning only considering the sample weight to reduce the influence of outliers, our method takes into account the sample weight to remove outliers not to involve the process of the model construction as well as solve the problem of imbalance classification.…”

“…Moreover, high-dimensional features for each imaging data are often extracted, by considering to capture the comprehensive changes of the disease. Hence, both of scenarios often result in the issue of over-fitting and the issue of curse of dimensionality ( Hu, Zhu, Zhu, Gan, 2020 , Zhu, Suk, Shen, 2014 ).…”

“…In the literature, few study has focused on exploring the above issues simultaneously. For example, the studies separately conduct feature selection ( Zhu et al., 2017b ) and sample weight ( Hu, Zhu, Zhu, Gan, 2020 , Zhu, Yang, Zhang, Zhang, 2019 ), while ( Zhu et al., 2014 ) proposed to conduct joint classification and regression. Recently, a few studies simultaneously conduct feature selection and sample selection ( Adeli et al., 2020w , Hu, Zhu, Zhu, Gan, 2020 ).…”

Deep understanding of big multimedia data

“…However, the performance of data understanding is heavily dependent on the learning techniques. In addition to volume, data are naturally comprised of multiple representations or sources in real applications in which multi-source data provide enriched information from different perspectives [3,4]. Multi-source data understanding is hence one of the most interesting and hottest topics in research and business nowadays and is remarkably useful for practical applications.…”