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 1176–1200 of 3304 papers

TitleStatusHype
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes—0
Efficient NTK using Dimensionality Reduction—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability—0
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings—0
Evaluation of company investment value based on machine learning—0
Evaluation of distance-based approaches for forensic comparison: Application to hand odor evidence—0
Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition—0
Evaluation of PPG Biometrics for Authentication in different states—0
Event detection in Colombian security Twitter news using fine-grained latent topic analysis—0
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Evolutionary Echo State Network: evolving reservoirs in the Fourier space—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Exact Post-selection Inference For Tracking S&P500—0
Exchangeability, Conformal Prediction, and Rank Tests—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
Exemplar-Centered Supervised Shallow Parametric Data Embedding—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Expected Information Gain Estimation via Density Approximations: Sample Allocation and Dimension Reduction—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond—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