Unsupervised feature selection with graph learning via low-rank constraint
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-017-5207-7
Abstract: Feature selection is one of the most important machine learning procedure, and it has been successfully applied to make a preprocessing before using classification and clustering methods. High-dimensional features often appear in big data, and… read more here.
Keywords: feature; feature selection; graph learning; low rank ... See more keywords
Essential multi-view graph learning for clustering
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-021-03002-5
Abstract: Multi-view clustering utilizes information from diverse views to improve the performance of clustering. For most existing multi-view spectral clustering methods, information of different views is integrated by pursuing a consensus similarity matrix for clustering. However,… read more here.
Keywords: graph learning; multi view; view; view graph ... See more keywords
Clustering with Similarity Preserving
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.07.086
Abstract: Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation… read more here.
Keywords: graph learning; similarity preserving; similarity; graph ... See more keywords
Clustering via Adaptive and Locality-constrained Graph Learning and Unsupervised ELM
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.03.045
Abstract: Abstract In this paper an effective graph learning method is proposed for clustering based on adaptive graph regularizations. Many graph learning methods focus on optimizing a global constraint on sparsity, low-rankness or weighted pair-wise distances,… read more here.
Keywords: elm; graph learning; locality; clustering via ... See more keywords
Smooth graph learning for functional connectivity estimation
Sign Up to like & getrecommendations! Published in 2021 at "NeuroImage"
DOI: 10.1016/j.neuroimage.2021.118289
Abstract: Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) signals is important in understanding neural representation and information processing in cortical networks. However, due to a lack of "ground truth" FC pattern, the reliability… read more here.
Keywords: graph learning; graph; smooth graph; sgfc ... See more keywords
AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins.
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac077
Abstract: Ligand molecules naturally constitute a graph structure. Recently, many excellent deep graph learning (DGL) methods have been proposed and used to model ligand bioactivities, which is critical for the virtual screening of drug hits from… read more here.
Keywords: graph learning; generalization; ligand; model ... See more keywords
scSGL: Kernelized Signed Graph Learning for Single-Cell Gene Regulatory Network Inference.
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac288
Abstract: MOTIVATION Elucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational… read more here.
Keywords: graph learning; scsgl; topology; gene ... See more keywords
Dimensionality Reduction of Hyperspectral Imagery Using Sparse Graph Learning
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2016.2606578
Abstract: Combining with sparse representation, the sparse graph can adaptively capture the intrinsic structural information of the specified data. In this paper, an unsupervised sparse-graph-learning-based dimensionality reduction (SGL-DR) method is proposed for hyperspectral image. In SGL-DR,… read more here.
Keywords: information; graph learning; sparse graph; graph ... See more keywords
Fast Spectral Embedded Clustering Based on Structured Graph Learning for Large-Scale Hyperspectral Image
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3035677
Abstract: Hyperspectral image (HSI) contains rich spectral information and spatial features, but the huge amount of data often leads to problems of low clustering accuracy and large computational complexity. In this letter, a new clustering method… read more here.
Keywords: graph; graph learning; hyperspectral image; fast spectral ... See more keywords
Graph Learning-Based Cooperative Spectrum Sensing in Cognitive Radio Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3219413
Abstract: In cooperative spectrum sensing (CSS), received signal strengths (RSSs) of multiple secondary users (SUs) are combined to improve sensing performance. In existing CSS schemes, RSS levels are often assumed in the same order of magnitude,… read more here.
Keywords: spectrum sensing; based cooperative; learning based; cooperative spectrum ... See more keywords
SF-SGL: Solver-Free Spectral Graph Learning From Linear Measurements
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2022.3198513
Abstract: This work introduces a highly scalable spectral graph densification (SGL) framework for learning resistor networks with linear measurements, such as node voltages and currents. We show that the proposed graph learning approach is equivalent to… read more here.
Keywords: solver free; spectral graph; linear measurements; voltage ... See more keywords