We report a complete deep-learning framework using a single-step object detection model
in order to quickly and accurately detect and classify the types of manufacturing defects …

Of late, there are many studies on the robust discriminant analysis, which adopt L 1-norm as
the distance metric, but their results are not robust enough to gain universal acceptance. To …

Linear Discriminant Analysis (LDA) is one of the most successful supervised dimensionality
reduction methods and has been widely used in many real-world applications. However, l 2 …

In this paper, we propose a framework for functional connectivity network (FCN) analysis,
which conducts the brain disease diagnosis on the resting state functional magnetic …

Recently, L1-norm-based discriminant subspace learning has attracted much more attention
in dimensionality reduction and machine learning. However, most existing approaches solve …

As one of the most popular supervised dimensionality reduction methods, linear discriminant
analysis (LDA) has been widely studied in machine learning community and applied to …

The regression loss function is a key factor in the training and optimization process of object
detection. The current mainstream regression loss functions are Ln norm loss, IOU loss and …

Talking Face Generation (TFG) reconstructs facial motions concerning lips given speech
input, which aims to generate highquality, synchronized, and lip-readable videos. Previous …

Twin support vector machine (TWSVM) is a classical and effective classifier for binary
classification. However, its robustness cannot be guaranteed due to the utilization of …

Linear discriminant analysis (LDA) has been widely used to extract features to solve various
machine learning problems. However, the current LDA methods can be confused by highly …