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 1751–1775 of 3304 papers

TitleStatusHype
Parallel Transport Unfolding: A Connection-based Manifold Learning Approach—0
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings—0
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach—0
Parameter Optimization using high-dimensional Bayesian Optimization—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Parametrization of stochastic inputs using generative adversarial networks with application in geology—0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
PCA-Based Out-of-Sample Extension for Dimensionality Reduction—0
PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes—0
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—0
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty—0
Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off—0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks—0
People Tracking with the Laplacian Eigenmaps Latent Variable Model—0
Perceptual Visual Interactive Learning—0
Perfecting Liquid-State Theories with Machine Intelligence—0
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model—0
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering—0
Performance prediction of data streams on high-performance architecture—0
Permutation invariant functions: statistical tests, density estimation, and computationally efficient embedding—0
Personalising Mobile Advertising Based on Users Installed Apps—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