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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 1101–1125 of 3304 papers

TitleStatusHype
involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs—0
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth—0
Non-Negative Matrix Factorization with Scale Data Structure Preservation—0
Algorithm-Agnostic Interpretations for Clustering—0
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Game-theoretic Objective Space PlanningCode0
Bayesian Optimization of Sampling Densities in MRICode1
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMsCode0
Vision Transformers for Action Recognition: A Survey—0
Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions—0
Dimensionality Reduction using Elastic Measures—0
Risk of Bias in Chest Radiography Deep Learning Foundation ModelsCode1
A Survey on Generative Diffusion ModelCode2
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—0
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations—0
Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A HumanCode0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—0
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmsCode1
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional AutoencodersCode1
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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