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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1526–1550 of 3304 papers

TitleStatusHype
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—0
High-Order Low-Rank Tensors for Semantic Role Labeling—0
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts—0
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
An efficient real-time target tracking algorithm using adaptive feature fusion—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Hodge Laplacians and Hodge Diffusion Maps—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Horizontal and Vertical Attention in Transformers—0
Efficient channel charting via phase-insensitive distance computation—0
Brain Inspired Face Recognition: A Computational Framework—0
How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?—0
How Do People Differ? A Social Media Approach—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
Human Activity Recognition using Smartphone—0
Human Activity Recognition using Smartphones—0
Human brain activity for machine attention—0
Human Face Recognition using Line Features—0
Efficient Binary Embedding of Categorical Data using BinSketch—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