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 2276–2300 of 3304 papers

TitleStatusHype
Style Transfer with Time Series: Generating Synthetic Financial Data—0
Towards Making High Dimensional Distance Metric Learning Practical—0
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE—0
Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging—0
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations—0
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations—0
Trace transform based method for color image domain identification—0
Trainable Compound Activation Functions for Machine Learning—0
Training Artificial Neural Networks by Coordinate Search Algorithm—0
Training Echo State Networks with Regularization through Dimensionality Reduction—0
Training (Overparametrized) Neural Networks in Near-Linear Time—0
Training-Time Attacks against k-Nearest Neighbors—0
Transcription Factor-DNA Binding Via Machine Learning Ensembles—0
Transfer Joint Matching for Unsupervised Domain Adaptation—0
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks—0
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images—0
Transfer Representation Learning with TSK Fuzzy System—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Transformation Based Deep Anomaly Detection in Astronomical Images—0
Transformer-based dimensionality reduction—0
Transform Invariant Auto-encoder—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
TransG : A Generative Model for Knowledge Graph Embedding—0
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions—0
Traversing the Subspace of Adversarial Patches—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