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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 1501–1525 of 3304 papers

TitleStatusHype
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised LearningCode0
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering—0
fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering—0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey—0
Interactive Dimensionality Reduction for Comparative AnalysisCode0
Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution—0
Self-paced Principal Component Analysis—0
Feature Grouping and Sparse Principal Component Analysis with Truncated RegularizationCode0
Regularisation for PCA- and SVD-type matrix factorisations—0
Efficient Tensor Contraction via Fast Count Sketch—0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Bounds on Causal Effects and Application to High Dimensional Data—0
Objective discovery of dominant dynamical processes with intelligible machine learningCode0
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching—0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate CurvatureCode1
Deep Learning for Functional Data Analysis with Adaptive Basis LayersCode1
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks—0
Topological Indoor Mapping through WiFi Signals—0
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
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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