Effective node selection technique towards sparse learning
Sign Up to like & getrecommendations! Published in 2020 at "Applied Intelligence"
DOI: 10.1007/s10489-020-01720-5
Abstract: Neural networks are getting wider and deeper to achieve state-of-the-art results in various machine learning domains. Such networks result in complex structures, high model size, and computational costs. Moreover, these networks are failing to adapt… read more here.
Keywords: effective node; sparse learning; selection technique; node selection ... See more keywords
A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Aerospace and Electronic Systems"
DOI: 10.1109/taes.2020.2988960
Abstract: In this article, we address the problem of detecting multiple noise-like jammers (NLJs) through a radar system equipped with an array of sensors. To this end, we develop an elegant and systematic framework wherein two… read more here.
Keywords: noise like; sparse learning; like jammers; approach ... See more keywords
A Sparse Learning Framework for Joint Effect Analysis of Copy Number Variants
Sign Up to like & getrecommendations! Published in 2017 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2015.2462332
Abstract: Copy number variants (CNVs), including large deletions and duplications, represent an unbalanced change of DNA segments. Abundant in human genomes, CNVs contribute to a large proportion of human genetic diversity, with impact on many human… read more here.
Keywords: joint effect; analysis; effect; sparse learning ... See more keywords
Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2018.2833487
Abstract: Imaging genetics has attracted significant interests in recent studies. Traditional work has focused on mass-univariate statistical approaches that identify important single nucleotide polymorphisms (SNPs) associated with quantitative traits (QTs) of brain structure or function. More… read more here.
Keywords: mri derived; guided sparse; tree guided; candidate genetic ... See more keywords
A Survey on Sparse Learning Models for Feature Selection.
Sign Up to like & getrecommendations! Published in 2020 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2020.2982445
Abstract: Feature selection is important in both machine learning and pattern recognition. Successfully selecting informative features can significantly increase learning accuracy and improve result comprehensibility. Various methods have been proposed to identify informative features from high-dimensional… read more here.
Keywords: learning models; feature selection; models feature; survey ... See more keywords
Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2021.3067137
Abstract: Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is… read more here.
Keywords: constrained sparse; robust rank; rank constrained; graph ... See more keywords
Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3251748
Abstract: In machine learning and statistics, the penalized regression methods are the main tools for variable selection (or feature selection) in high-dimensional sparse data analysis. Due to the nonsmoothness of the associated thresholding operators of commonly… read more here.
Keywords: penalty; newton raphson; sparse learning; sparsity sparse ... See more keywords
Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2016.2567399
Abstract: Supervised learning over graphs is an intrinsically difficult problem: simultaneous learning of relevant features from the complete subgraph feature set, in which enumerating all subgraph features occurring in given graphs is practically intractable due to… read more here.
Keywords: subgraph feature; sparse learning; generalized sparse; complete subgraph ... See more keywords
Greedy Sparse Learning Over Network
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2017.2710905
Abstract: In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted… read more here.
Keywords: learning network; sparse learning; greedy sparse; network ... See more keywords
Federated Optimization of ℓ0-norm Regularized Sparse Learning
Sign Up to like & getrecommendations! Published in 2022 at "Algorithms"
DOI: 10.3390/a15090319
Abstract: Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their capability of recovering… read more here.
Keywords: regularized sparse; federated optimization; hard thresholding; sparse learning ... See more keywords