SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1701–1750 of 3304 papers

TitleStatusHype
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Enhanced Sampling with Machine Learning: A Review—0
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images—0
Enhancing IoT Security Against DDoS Attacks through Federated Learning—0
Enhancing literature review with LLM and NLP methods. Algorithmic trading case—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Enhancing Robustness of Machine Learning Systems via Data Transformations—0
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension—0
Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm—0
Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders—0
Enhancing UAV Path Planning Efficiency Through Accelerated Learning—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Ensembles of Classifiers based on Dimensionality Reduction—0
Entangled Kernels -- Beyond Separability—0
Entangled Mean Estimation in High-Dimensions—0
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes—0
Error Metrics for Learning Reliable Manifolds from Streaming Data—0
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare—0
ESPACE: Dimensionality Reduction of Activations for Model Compression—0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
Estimating a Manifold from a Tangent Bundle Learner—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Estimating covariance and precision matrices along subspaces—0
Estimating Model Uncertainty of Neural Networks in Sparse Information Form—0
Estimating Model Uncertainty of Neural Network in Sparse Information Form—0
Estimation of Cross-Sectional Dependence in Large Panels—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Evaluating the Stability of Deep Learning Latent Feature Spaces—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
Evolutionary Echo State Network: evolving reservoirs in the Fourier space—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
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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