SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1651–1700 of 3304 papers

TitleStatusHype
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization—0
Efficient and Scalable Batch Bayesian Optimization Using K-Means—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Efficient channel charting via phase-insensitive distance computation—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—0
Efficient Fair Principal Component Analysis—0
Efficient GPU implementation of randomized SVD and its applications—0
Efficient L1-Norm Principal-Component Analysis via Bit Flipping—0
Efficient Large-Scale Similarity Search Using Matrix Factorization—0
Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability—0
Efficient Learning and Planning with Compressed Predictive States—0
Efficiently Computing Similarities to Private Datasets—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Efficient NTK using Dimensionality Reduction—0
Efficient Optimization for Discriminative Latent Class Models—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
Efficient Sketching Algorithm for Sparse Binary Data—0
Efficient Sparse PCA via Block-Diagonalization—0
Efficient Tensor Contraction via Fast Count Sketch—0
Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
EigenGS Representation: From Eigenspace to Gaussian Image Space—0
ELA: Efficient Local Attention for Deep Convolutional Neural Networks—0
Elastic deep autoencoder for text embedding clustering by an improved graph regularization—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Elementos da teoria de aprendizagem de máquina supervisionada—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth—0
Embedding Compression for Efficient Re-Identification—0
Embedding Feature Selection for Large-scale Hierarchical Classification—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Empirical Evaluation of Kernel PCA Approximation Methods in Classification Tasks—0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
Empowering individual trait prediction using interactions—0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Emulators for stellar profiles in binary population modeling—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks—0
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes—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