SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2521–2530 of 3304 papers

TitleStatusHype
Non-Gaussian Component Analysis using Entropy Methods—0
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversionCode0
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
Learning Low-Dimensional Temporal Representations—0
Using pseudo-senses for improving the extraction of synonyms from word embeddings—0
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level GenerationCode0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA—0
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral ImageryCode0
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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