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 1151–1200 of 3304 papers

TitleStatusHype
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
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images—0
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes—0
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images—0
Extracting Geography from Trade Data—0
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—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
An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf Optimizer (GWO) for text feature selection and clustering—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
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