SOTAVerified

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 401–425 of 3304 papers

TitleStatusHype
A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms—0
A Light weight and Hybrid Deep Learning Model based Online Signature Verification—0
Asteroids co-orbital motion classification based on Machine Learning—0
AstroM^3: A self-supervised multimodal model for astronomy—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
A study of semantic augmentation of word embeddings for extractive summarization—0
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting—0
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data—0
A survey of dimensionality reduction techniques—0
A survey of dimensionality reduction techniques based on random projection—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
A Survey on Archetypal Analysis—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
Approximate Matrix Multiplication with Application to Linear Embeddings—0
Alternating Co-Quantization for Cross-Modal Hashing—0
A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization—0
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis—0
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction—0
A Systematic Study of Semantic Vector Space Model Parameters—0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—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