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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 2476–2500 of 3304 papers

TitleStatusHype
A convex formulation for high-dimensional sparse sliced inverse regression—0
Visualization of High-dimensional Scalar Functions Using Principal ParameterizationsCode0
Randomized Iterative Algorithms for Fisher Discriminant Analysis—0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
Mixtures of Skewed Matrix Variate Bilinear Factor AnalyzersCode0
A note on concentration inequality for vector-valued martingales with weak exponential-type tails—0
Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data—0
Geometry of Deep Learning for Magnetic Resonance Fingerprinting—0
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation—0
A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations—0
Scalable Manifold Learning for Big Data with Apache SparkCode0
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti—0
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMSCode0
Parameter-wise co-clustering for high-dimensional data—0
XPCA: Extending PCA for a Combination of Discrete and Continuous Variables—0
Vehicles Lane-changing Behavior Detection—0
Supervised Kernel PCA For Longitudinal Data—0
Fourier analysis perspective for sufficient dimension reduction problem—0
A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression—0
DNN Feature Map Compression using Learned Representation over GF(2)Code0
Efficient Outlier Removal in Large Scale Global Structure-from-MotionCode0
Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study—0
Low-complexity 8-point DCT Approximation Based on Angle Similarity for Image and Video Coding—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Hybrid Subspace Learning for High-Dimensional Data—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