Discriminative subspace matrix factorization for multiview data clustering
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 …
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
Pairwise dependence-based unsupervised feature selection
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 …
times and large memory requirements, many machine learning methods have difficulty in …
Dual space latent representation learning for unsupervised feature selection
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 …
but also interconnected with each other. However, the interconnection information has not …
Stable feature selection using copula based mutual information
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 …
methods of feature selection address the problem by devising some scoring function while …
Feature selection via non-convex constraint and latent representation learning with laplacian embedding
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 …
and the interconnection information between the data is not fully utilized. In order to solve …
Uncorrelated feature selection via sparse latent representation and extended OLSDA
Modern unsupervised feature selection methods predominantly obtain the cluster structure
and pseudo-labels information through spectral clustering. However, the pseudo-labels …
and pseudo-labels information through spectral clustering. However, the pseudo-labels …
Bi-level ensemble method for unsupervised feature selection
Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …
increasingly more attention. However, due to the absence of labels, unsupervised feature …
Multi-view unsupervised feature selection with tensor robust principal component analysis and consensus graph learning
Recently, multi-view unsupervised feature selection has attracted much attention due to its
efficiency and better interpretability in processing high-dimensional multi-view datasets …
efficiency and better interpretability in processing high-dimensional multi-view datasets …
Training data independent image registration using generative adversarial networks and domain adaptation
D Mahapatra, Z Ge - Pattern Recognition, 2020 - Elsevier
Medical image registration is an important task in automated analysis of multimodal images
and temporal data involving multiple patient visits. Conventional approaches, although …
and temporal data involving multiple patient visits. Conventional approaches, although …
Adaptive local linear discriminant analysis
Dimensionality reduction plays a significant role in high-dimensional data processing, and
Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction …
Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction …