SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1251–1275 of 3304 papers

TitleStatusHype
Fair Kernel Learning—0
Fair Principal Component Analysis and Filter Design—0
Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
ABID: Angle Based Intrinsic Dimensionality—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Fast and Large-scale Unsupervised Relation Extraction—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Fast clustering for scalable statistical analysis on structured images—0
Broadband Beamforming via Linear Embedding—0
Fast Data-driven Greedy Sensor Selection for Ridge Regression—0
Fast Data-independent KLT Approximations Based on Integer Functions—0
Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning—0
Faster Discovery of Faster System Configurations with Spectral Learning—0
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training—0
Faster variational inducing input Gaussian process classification—0
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming—0
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method—0
Fast Multi-Group Gaussian Process Factor Models—0
Feature selection or extraction decision process for clustering using PCA and FRSD—0
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks—0
Fast Robust PCA on Graphs—0
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—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