SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1001–1050 of 3304 papers

TitleStatusHype
Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement—0
Bayesian nonparametric Principal Component Analysis—0
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
An Impossibility Theorem for Node Embedding—0
A Flexible Iterative Framework for Consensus Clustering—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints—0
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation—0
BDEC:Brain Deep Embedded Clustering model—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—0
Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
An Image-Based Fluid Surface Pattern Model—0
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein—0
Distributional Reference Class Forecasting of Corporate Sales Growth With Multiple Reference Variables—0
Distributional Semantics in R with the wordspace Package—0
Distribution-based Label Space Transformation for Multi-label Learning—0
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
Efficient NTK using Dimensionality Reduction—0
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes—0
Circular Coordinate Methods with Generalized Penalty Functions—0
Angular Embedding: A New Angular Robust Principal Component Analysis—0
Efficient Optimization for Discriminative Latent Class Models—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Chiron: A Robust Recommendation System with Graph Regularizer—0
Angle constrained path to cluster multiple manifolds—0
Chasing Collective Variables using Autoencoders and biased trajectories—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
A Comprehensive Filter Feature Selection for Improving Document Classification—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Channel Charting: Locating Users within the Radio Environment using Channel State Information—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
Efficient L1-Norm Principal-Component Analysis via Bit Flipping—0
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images—0
Efficient Large-Scale Similarity Search Using Matrix Factorization—0
Efficiently Computing Similarities to Private Datasets—0
A new set of cluster driven composite development indicators—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data—0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
Efficient GPU implementation of randomized SVD and its applications—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
EigenGS Representation: From Eigenspace to Gaussian Image Space—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