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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 1826–1850 of 3304 papers

TitleStatusHype
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Deep matrix factorizations—0
Evaluation of company investment value based on machine learning—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Parametric UMAP embeddings for representation and semi-supervised learningCode3
Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)—0
Deep Learning of Individual AestheticsCode1
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality ReductionCode1
Compact Learning for Multi-Label Classification—0
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and SurveyCode0
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying densityCode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data ProcessingCode0
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
PCA Reduced Gaussian Mixture Models with Applications in SuperresolutionCode0
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Sufficient Dimension Reduction for Average Causal Effect Estimation—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—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