SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2076–2100 of 3304 papers

TitleStatusHype
A Fully Convolutional Network for MR Fingerprinting—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—0
TMI: Thermodynamic inference of data manifolds—0
Gradient-based Sparse Principal Component Analysis with Extensions to Online LearningCode0
Event detection in Colombian security Twitter news using fine-grained latent topic analysis—0
Safe squeezing for antisparse codingCode0
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Topological Stability: a New Algorithm for Selecting The Nearest Neighbors in Non-Linear Dimensionality Reduction Techniques—0
Efficient Fair Principal Component Analysis—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity—0
Decoding Imagined Speech and Computer Control using Brain Waves—0
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
An automated approach for task evaluation using EEG signals—0
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity—0
News2vec: News Network Embedding with Subnode Information—0
Privacy-Preserving Machine Learning Using EtC Images—0
Solving NMF with smoothness and sparsity constraints using PALMCode0
A Comparison of Neural Network Training Methods for Text Classification—0
Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies—0
Variational Quantum Algorithms for Dimensionality Reduction and Classification—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