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 1776–1800 of 3304 papers

TitleStatusHype
Persuasion by Dimension Reduction—0
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning—0
PFAx: Predictable Feature Analysis to Perform Control—0
Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design—0
Phone-based Metric as a Predictor for Basic Personality Traits—0
Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra—0
Physical Action Categorization using Signal Analysis and Machine Learning—0
Physically interpretable machine learning algorithm on multidimensional non-linear fields—0
Physically Interpretable Representation and Controlled Generation for Turbulence Data—0
Physics-aware, probabilistic model order reduction with guaranteed stability—0
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design—0
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures—0
Pinning control of networks: dimensionality reduction through simultaneous block-diagonalization of matrices—0
PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization—0
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition—0
Pointed subspace approach to incomplete data—0
Policy Optimization for PDE Control with a Warm Start—0
Policy Search with High-Dimensional Context Variables—0
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach—0
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation—0
Practical Algorithms for Learning Near-Isometric Linear Embeddings—0
Precise expressions for random projections: Low-rank approximation and randomized Newton—0
Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI—0
Precision-Recall Balanced Topic Modelling—0
Predicting Inflation with Recurrent Neural Networks—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