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 1601–1625 of 3304 papers

TitleStatusHype
Implicit Sparse Code Hashing—0
Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics—0
Information-Theoretic Representation Learning for Positive-Unlabeled Classification—0
Improved Gaussian-Bernoulli Restricted Boltzmann Machines for UAV-Ground Communication Systems—0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Improving evaluation and optimization of MT systems against MEANT—0
Improving information retrieval through correspondence analysis instead of latent semantic analysis—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model—0
Efficient and Scalable Batch Bayesian Optimization Using K-Means—0
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis—0
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer—0
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization—0
Improving the Projection of Global Structures in Data through Spanning Trees—0
Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements—0
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning—0
IMU-based Modularized Wearable Device for Human Motion Classification—0
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Bounds on Causal Effects and Application to High Dimensional Data—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