SOTAVerified

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 1226–1250 of 3304 papers

TitleStatusHype
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices—0
Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction—0
Deep Compressed Learning for 3D Seismic Inversion—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Are Latent Factor Regression and Sparse Regression Adequate?—0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap—0
A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Decoding Imagined Speech and Computer Control using Brain Waves—0
Decoding Emotional Experience through Physiological Signal Processing—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
Adapting Speaker Embeddings for Speaker Diarisation—0
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—0
A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model—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