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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2226–2250 of 3304 papers

TitleStatusHype
Spaceland Embedding of Sparse Stochastic Graphs—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Wavelet Based Dictionaries for Piecewise Dimensionality Reduction of ECG Signals—0
Sparse Representation of 3D Images for Piecewise Dimensionality Reduction with High Quality Reconstruction—0
Integrative Factorization of Bidimensionally Linked MatricesCode0
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
The spiked matrix model with generative priorsCode0
Adapting Text Embeddings for Causal InferenceCode1
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
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
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
One-bit LFMCW Radar: Spectrum Analysis and Target Detection—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
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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