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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 2251–2275 of 3304 papers

TitleStatusHype
Learning Clustered Representation for Complex Free Energy Landscapes—0
Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers—0
Multi-Frequency Vector Diffusion Maps—0
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge IntelligenceCode0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Unsupervised and Supervised Principal Component Analysis: TutorialCode0
Linear and Quadratic Discriminant Analysis: TutorialCode0
A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
The spiked matrix model with generative priorsCode0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
Attention-based Supply-Demand Prediction for Autonomous Vehicles—0
Style Transfer with Time Series: Generating Synthetic Financial Data—0
Visualization of AE's Training on Credit Card Transactions with Persistent Homology—0
Deep-gKnock: nonlinear group-feature selection with deep neural network—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
One-bit LFMCW Radar: Spectrum Analysis and Target Detection—0
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces—0
CASS: Cross Adversarial Source Separation via Autoencoder—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Multi-view Locality Low-rank Embedding for Dimension Reduction—0
Comparison of Machine Learning Models in Food Authentication Studies—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