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 1251–1300 of 3304 papers

TitleStatusHype
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
Identifying Selections Operating on HIV-1 Reverse Transcriptase via Uniform Manifold Approximation and ProjectionCode0
On The Relative Error of Random Fourier Features for Preserving Kernel Distance—0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
A canonical correlation-based framework for performance analysis of radio access networks—0
Patients' Severity States Classification based on Electronic Health Record (EHR) Data using Multiple Machine Learning and Deep Learning ApproachesCode0
Spectral Diffusion Processes—0
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach—0
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)—0
On Projections to Linear SubspacesCode0
involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs—0
Non-Negative Matrix Factorization with Scale Data Structure Preservation—0
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth—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
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
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations—0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—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
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—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 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
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set methodCode0
Collaborative causal inference on distributed data—0
Training-Time Attacks against k-Nearest Neighbors—0
On a Mechanism Framework of Autoencoders—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
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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