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
Fast unsupervised feature selection with anchor graph and ℓ2,1-norm regularization
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-017-5582-0
Abstract: Graph-based unsupervised feature selection has been proven to be effective in dealing with unlabeled and high-dimensional data. However, most existing methods face a number of challenges primarily due to their high computational complexity. In light… read more here.
Keywords: graph; feature selection; unsupervised feature; anchor graph ... See more keywords
Filter-based unsupervised feature selection using Hilbert–Schmidt independence criterion
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-018-0869-7
Abstract: Feature selection is a fundamental preprocess before performing actual learning; especially in unsupervised manner where the data are unlabeled. Essentially, when there are too many features in the problem, dimensionality reduction through discarding weak features… read more here.
Keywords: unsupervised feature; feature selection; schmidt independence; feature ... See more keywords
Unsupervised feature selection via Diversity-induced Self-representation
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DOI: 10.1016/j.neucom.2016.09.043
Abstract: Feature selection is to select a subset of relevant features from the original feature set. In practical applications, regarding the unavailability of an amount of the labels is still a challenging problem. To overcome this… read more here.
Keywords: feature selection; feature; diversity; self representation ... See more keywords
KSUFS: A Novel Unsupervised Feature Selection Method Based on Statistical Tests for Standard and Big Data Problems
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2930355
Abstract: The typical inaccuracy of data gathering and preparation procedures makes erroneous and unnecessary information to be a common issue in real-world applications. In this context, feature selection methods are used in order to reduce the… read more here.
Keywords: unsupervised feature; feature selection; feature; big data ... See more keywords
Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3207930
Abstract: Unsupervised feature selection aims to derive a compact set of features with desired generalization ability via removing the irrelevant and redundant features, yet challenging due to the unavailability of labels. Works about unsupervised feature selection… read more here.
Keywords: rank; low rank; feature; novel low ... See more keywords
Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2017.2737823
Abstract: For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for… read more here.
Keywords: feature learning; image; classification; hyperspectral image ... See more keywords
Unsupervised Feature Learning Using Recurrent Neural Nets for Segmenting Hyperspectral Images
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3013205
Abstract: Although deep learning is gaining more widespread use in hyperspectral image analysis, it is challenging to train high-capacity models in a supervised way--ground-truth sets are expensive to obtain, and they are practically always extremely imbalanced.… read more here.
Keywords: feature learning; using recurrent; recurrent neural; learning using ... See more keywords
Desert Seismic Signal Denoising Based on Unsupervised Feature Learning and Time–Frequency Transform Technique
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3067645
Abstract: Noise reduction is an essential step in seismic exploration. Formally, the random broadband noise in the desert seismic is characterized as nonlinear, nonstationary, and non-Gaussian, and the energy is concentrated mainly in the low-frequency range.… read more here.
Keywords: noise; frequency; desert seismic; feature learning ... See more keywords
Unsupervised Feature Selection Using an Integrated Strategy of Hierarchical Clustering with Singular Value Decomposition: An Integrative Biomarker Discovery Method with Application to Acute Myeloid Leukemia.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2021.3110989
Abstract: Here we propose a novel unsupervised feature selection by combining hierarchical feature clustering with singular value decomposition (SVD). The proposed algorithm first generates several feature clusters by adopting hierarchical clustering on the feature space and… read more here.
Keywords: unsupervised feature; singular value; feature selection; feature ... See more keywords
Two-Dimensional Unsupervised Feature Selection via Sparse Feature Filter.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3162908
Abstract: Unsupervised feature selection is a vital yet challenging topic for effective data learning. Recently, 2-D feature selection methods show good performance on image analysis by utilizing the structure information of image. Current 2-D methods usually… read more here.
Keywords: feature filter; feature; feature selection; unsupervised feature ... See more keywords