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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 1051–1075 of 3304 papers

TitleStatusHype
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network—0
Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm—0
A Unified Framework for Optimization-Based Graph Coarsening—0
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Dimensionality-reduced subspace clustering—0
DimenFix: A novel meta-dimensionality reduction method for feature preservation—0
Augment on Manifold: Mixup Regularization with UMAP—0
Digital Fingerprinting of Microstructures—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Diffusion Maps : Using the Semigroup Property for Parameter Tuning—0
Diffusion Maps meet Nyström—0
Audio Visual Speech Recognition using Deep Recurrent Neural Networks—0
A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations—0
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis—0
Diffusion Fingerprints—0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals—0
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks—0
A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition—0
A Method for Classifying Snow Using Ski-Mounted Strain Sensors—0
Difficulty in estimating visual information from randomly sampled images—0
Differential Privacy Meets Neural Network Pruning—0
Differential Privacy for Clustering Under Continual Observation—0
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—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