Partial label learning via low-rank representation and label propagation
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DOI: 10.1007/s00500-019-04269-9
Abstract: In partial label learning, each training instance is assigned with a set of candidate labels, among which only one is correct. An intuitive strategy to learn from such ambiguous data is disambiguation. Existing methods following… read more here.
Keywords: label; rank representation; low rank; via low ... See more keywords
Robust subspace learning-based low-rank representation for manifold clustering
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DOI: 10.1007/s00521-018-3617-8
Abstract: Spectral clustering-based subspace clustering methods have attracted broad interest in recent years. This kind of methods usually uses the self-representation in the original space to extract the affinity between the data points. However, we can… read more here.
Keywords: rank representation; manifold clustering; subspace; low rank ... See more keywords
Discriminative low-rank representation with Schatten-p norm for image recognition
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DOI: 10.1007/s11042-019-7653-x
Abstract: Low-rank representation (LRR) has attracted much attention recently due to its efficacy in a rich variety of real world applications. Recently, the non-convex regularization has become widely used in the rank minimization problem. In this… read more here.
Keywords: rank representation; rank; recognition; low rank ... See more keywords
An Efficient Method of Hyperspectral Image Dimension Reduction Based on Low Rank Representation and Locally Linear Embedding
Sign Up to like & getrecommendations! Published in 2020 at "Integrated Ferroelectrics"
DOI: 10.1080/10584587.2020.1728626
Abstract: Abstract Hyperspectralimages (HSIs) can provide powerful spectral discriminative information for the land-covers, thus is widely used in classification and target detection. However, HSIs always suffer from the curse of high dimensionality due the high spectral… read more here.
Keywords: rank representation; dimension; representation locally; dimension reduction ... See more keywords
Semi-Supervised hyperspectral image classification using local low-rank representation
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DOI: 10.1080/2150704x.2018.1538581
Abstract: ABSTRACT In the area of hyperspectral image (HSI) classification, graph-based semi-supervised learning (SSL) has been proved to be highly effective. Constructing a proper graph is critical for graph-based SSL tasks. In HSI, spectral distance is… read more here.
Keywords: rank representation; classification; semi supervised; graph ... See more keywords
Iterative Reconstrained Low-Rank Representation via Weighted Nonconvex Regularizer
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DOI: 10.1109/access.2018.2870371
Abstract: Benefiting from the joint consideration of geometric structures and low-rank constraint, graph low-rank representation (GLRR) method has led to the state-of-the-art results in many applications. However, it faces the limitations that the structure of errors… read more here.
Keywords: rank representation; via weighted; iterative reconstrained; low rank ... See more keywords
Fusion of Low-Quality Visible and Infrared Images Based on Multi-Level Latent Low-Rank Representation Joint With Retinex Enhancement and Multi-Visual Weight Information
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DOI: 10.1109/access.2021.3139670
Abstract: Latent low-rank representation has been applied to multi-level image decomposition for the fusion of infrared and visible images to obtain good results. However, when the original infrared and visible images are of low quality, the… read more here.
Keywords: rank representation; fusion; low rank; multi level ... See more keywords
Hyperspectral Image Reconstruction by Latent Low-Rank Representation for Classification
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2018.2844555
Abstract: To effectively reduce the spectral variation that degrades classification performance, a novel low-rank subspace recovery method based on latent low-rank representation (LatLRR) is proposed for hyperspectral images in this letter. Different from the robust principal… read more here.
Keywords: rank representation; classification; latent low; low rank ... See more keywords
Hyperspectral Anomaly Detection Based on Low-Rank Representation Using Local Outlier Factor
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DOI: 10.1109/lgrs.2020.2994745
Abstract: In recent years, low-rank representation (LRR) has attracted considerable attention in the field of hyperspectral anomaly detection. The main objective of LRR-based methods is to extract anomalies from the complex background. However, the presence of… read more here.
Keywords: rank representation; low rank; detection based; hyperspectral anomaly ... See more keywords
Semisupervised Hyperspectral Band Selection Based on Dual-Constrained Low-Rank Representation
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DOI: 10.1109/lgrs.2021.3049267
Abstract: Band selection (BS) aims to choose a salient subset implied sufficient information from the numerous bands, which supplies a significantly efficient way to alleviate the barrier of dimensionality disaster for hyperspectral image classification (HSIC). This… read more here.
Keywords: low rank; band; selection; rank representation ... See more keywords
Hyperspectral Anomaly Detection via S1/2 Regularized Low Rank Representation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3063252
Abstract: Anomaly detection has been drawing a great deal of attention by virtue of its practicability among the hyperspectral research area. Low-rank representation (LRR) has been widely employed to detect anomalies from hyperspectral imagery (HSI) effectively… read more here.
Keywords: rank representation; low rank; inline formula; detection ... See more keywords