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 2101–2150 of 3304 papers

TitleStatusHype
Interacting with Massive Behavioral Data—0
Interactions between Representation Learning and Supervision—0
Interactive dimensionality reduction using similarity projections—0
Interactive Distillation of Large Single-Topic Corpora of Scientific Papers—0
Interactive Graphics for Visually Diagnosing Forest Classifiers in R—0
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein—0
Interpretability Beyond Classification Output: Semantic Bottleneck Networks—0
Interpretability Illusions in the Generalization of Simplified Models—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—0
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold—0
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs—0
Inteval Analysis for two spherical functions arising from robust Perspective-n-Lines problem—0
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network—0
Inverting Nonlinear Dimensionality Reduction with Scale-Free Radial Basis Function Interpolation—0
Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra—0
involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
Is Deep Learning Network Necessary for Image Generation?—0
Isolation Kernel and Its Effect on SVM—0
Isometric Autoencoders—0
Isometric sketching of any set via the Restricted Isometry Property—0
Is 'Unsupervised Learning' a Misconceived Term?—0
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces—0
Iterative Cluster Harvesting for Wafer Map Defect Patterns—0
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering—0
Statistical feature embedding for heart sound classification—0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise—0
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey—0
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces—0
Joint Characterization of Multiscale Information in High Dimensional Data—0
Joint Characterization of Spatiotemporal Data Manifolds—0
Joint Characterization of the Cryospheric Spectral Feature Space—0
Joint Dimensionality Reduction and Metric Learning: A Geometric Take—0
Joint Dimensionality Reduction for Separable Embedding Estimation—0
Joint Dimensionality Reduction for Two Feature Vectors—0
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling—0
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints—0
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization—0
Kernel methods for detecting coherent structures in dynamical data—0
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
Kernel Methods for the Approximation of Nonlinear Systems—0
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods—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