In a real-world scenario, an object is easily considered as features combined by multiple
views in reality. Thus, multiview features can be encoded into a unified and discriminative …

Many research topics present very high dimensional data. Because of the heavy execution
times and large memory requirements, many machine learning methods have difficulty in …

In real-world applications, data instances are not only related to high-dimensional features,
but also interconnected with each other. However, the interconnection information has not …

Feature selection is a key step in many machine learning tasks. A majority of the existing
methods of feature selection address the problem by devising some scoring function while …

In unsupervised feature selection, the relationship between pseudo-labels is often ignored,
and the interconnection information between the data is not fully utilized. In order to solve …

Modern unsupervised feature selection methods predominantly obtain the cluster structure
and pseudo-labels information through spectral clustering. However, the pseudo-labels …

Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …

Recently, multi-view unsupervised feature selection has attracted much attention due to its
efficiency and better interpretability in processing high-dimensional multi-view datasets …

Medical image registration is an important task in automated analysis of multimodal images
and temporal data involving multiple patient visits. Conventional approaches, although …

Dimensionality reduction plays a significant role in high-dimensional data processing, and
Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction …