Autoencoder-based unsupervised clustering and hashing
Sign Up to like & getrecommendations! Published in 2020 at "Applied Intelligence"
DOI: 10.1007/s10489-020-01797-y
Abstract: Faced with a large amount of data and high-dimensional data information in a database, the existing exact nearest neighbor retrieval methods cannot obtain ideal retrieval results within an acceptable retrieval time. Therefore, researchers have begun… read more here.
Keywords: autoencoder based; based unsupervised; clustering hashing; unsupervised clustering ... See more keywords
Image-based, unsupervised estimation of fish size from commercial landings using deep learning
Sign Up to like & getrecommendations! Published in 2019 at "Ices Journal of Marine Science"
DOI: 10.1093/icesjms/fsz216
Abstract: The dynamics of fish length distribution is a key input for understanding the fish population dynamics and taking informed management decisions on exploited stocks. Nevertheless, in most fisheries, the length of landed fish is still… read more here.
Keywords: based unsupervised; estimation; using deep; image based ... See more keywords
A Novel Similarity Based Unsupervised Technique for Training Convolutional Filters
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3277253
Abstract: Achieving satisfactory results with Convolutional Neural Networks (CNNs) depends on how effectively the filters are trained. Conventionally, an appropriate number of filters is carefully selected, the filters are initialized with a proper initialization method and… read more here.
Keywords: based unsupervised; similarity based; novel similarity; convolutional filters ... See more keywords
Hyperspectral Image Clustering Based on Unsupervised Broad Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2907598
Abstract: Due to the difficulty of labeling a large number of training samples of a hyperspectral image (HSI), unsupervised clustering methods have drawn great attention. The recently proposed broad learning (BL) can implement both linear and… read more here.
Keywords: hyperspectral image; based unsupervised; clustering based; broad learning ... See more keywords
Deep Prior-Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3044455
Abstract: Irregularity and coarse spatial sampling of seismic data strongly affect the performances of processing and imaging algorithms. Therefore, interpolation is a usual preprocessing step in most of the processing workflows. In this work, we propose… read more here.
Keywords: deep prior; prior based; based unsupervised; reconstruction irregularly ... See more keywords
Contrast-Based Unsupervised Hashing Learning With Multi-Hashcode
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2021.3130500
Abstract: Due to high storage and calculation efficiency, hash-based methods have been widely used in image retrieval systems. Unsupervised deep hashing methods can learn the binary representations of images effectively without any annotations. The strategy of… read more here.
Keywords: contrast based; hashing learning; unsupervised hashing; based unsupervised ... See more keywords
PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2022.3213669
Abstract: With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in the absence of high-quality, fully sampled datasets for training, the… read more here.
Keywords: contrastive representation; physics; representation learning; unsupervised contrastive ... See more keywords
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural Networks
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2020.3008844
Abstract: Hyperspectral images (HSIs) are useful for many remote sensing applications. However, they are usually affected by noise that degrades the HSIs quality. Therefore, HSI denoising is important to improve the performance of subsequent HSI processing… read more here.
Keywords: using sure; based unsupervised; sure cnn; cnn ... See more keywords
Optimal Decision Tree Based Unsupervised Learning Method for Data Clustering
Sign Up to like & getrecommendations! Published in 2017 at "International Journal of Intelligent Engineering and Systems"
DOI: 10.22266/ijies2017.0430.14
Abstract: Clustering is an investigative data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. Our investigation using a pattern based clustering on numerical data… read more here.
Keywords: based unsupervised; tree; tree based; optimal decision ... See more keywords
Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis
Sign Up to like & getrecommendations! Published in 2019 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2019.00864
Abstract: Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each… read more here.
Keywords: single cell; based unsupervised; cell; gene expression ... See more keywords
Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data
Sign Up to like & getrecommendations! Published in 2020 at "Genes"
DOI: 10.3390/genes11121493
Abstract: The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets,… read more here.
Keywords: based unsupervised; tensor decomposition; feature; decomposition based ... See more keywords