In the literature of mitigating unfairness in machine learning (ML), many fairness measures are designed to evaluate predictions of learning models and also utilized to guide the training of fair m…
We propose an inverted approach to the Sparse Principal Component Analysis (SPCA) problem. Most previous research efforts focused on solving the problem of maximizing the variance subject to sparsi…
Due to the inherent vulnerability of deep neural networks (DNNs), the adversarial example (AE) attack has become a serious threat to intelligent systems, e.g., the failure cause of an image classif…
Multiview data analysis provides an effective means to integrate the distinct information sources which are inherent to many applications. Data clustering in a multiview setting specifically aims t…
The matrix spectral norm and nuclear norm appear in enormous applications. The generalization of these norms to higher-order tensors is becoming increasingly important, but unfortunately they are N…
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature sel…
We study the problem of decomposing a polynomial p into a sum of r squares by minimizing a quadratically penalized objective f p(u) = || ∑r i=1 u2i-p||2. This objective is nonconvex and is equiva…
In contrast to the traditional feature selection (FS), local FS (LFS) partitions the whole sample space and obtains the feature subset for each local region. However, most existing LFS algorithms l…
With the emergence of deep neural networks, many research fields, such as image classification, object detection, speech recognition, natural language processing, machine translation, and automatic…
In traditional data stream mining, classification models are typically trained on labeled samples from a single source. However, in real-world scenarios, obtaining accurate labels is very hard and …