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 1101–1150 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
A novel approach for Fair Principal Component Analysis based on eigendecompositionCode0
GANs and Closures: Micro-Macro Consistency in Multiscale Modeling—0
Convergent autoencoder approximation of low bending and low distortion manifold embeddingsCode0
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
A Graphical Model for Fusing Diverse Microbiome DataCode0
IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimationCode1
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set methodCode0
Collaborative causal inference on distributed data—0
On a Mechanism Framework of Autoencoders—0
HEFT: Homomorphically Encrypted Fusion of Biometric TemplatesCode1
Training-Time Attacks against k-Nearest Neighbors—0
May the force be with you—0
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification—0
Quantum artificial vision for defect detection in manufacturing—0
Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network—0
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems—0
Factor Network Autoregressions—0
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints—0
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification—0
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data—0
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19Code0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization—0
FastSVD-ML-ROM: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications—0
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling—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