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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2526–2550 of 3304 papers

TitleStatusHype
Active Learning with TensorBoard Projector—0
Active Linear Regression for _p Norms and Beyond—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Adapting Speaker Embeddings for Speaker Diarisation—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Adaptive Locally Linear Embedding—0
Adaptive Metric Dimensionality Reduction—0
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Adaptive Randomized Dimension Reduction on Massive Data—0
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing—0
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery—0
A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction—0
A Data Quarantine Model to Secure Data in Edge Computing—0
A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing—0
Additive Component Analysis—0
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe—0
A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation—0
A Deep Graph Embedding Network Model for Face Recognition—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
A Deep Signed Directional Distance Function for Object Shape Representation—0
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified