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Dimensionality Reduction

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

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Papers

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Showing 1801–1850 of 3304 papers

TitleStatusHype
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
Auto-Encoding Variational Bayes for Inferring Topics and VisualizationCode0
Prediction of daily maximum ozone levels using Lasso sparse modeling method—0
Understanding Information Processing in Human Brain by Interpreting Machine Learning ModelsCode1
Interactive Latent Interpolation on MNIST DatasetCode0
Mycorrhiza: Genotype Assignment usingPhylogenetic NetworksCode0
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank EstimationCode0
Causal learning with sufficient statistics: an information bottleneck approach—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Circular Coordinate Methods with Generalized Penalty Functions—0
Challenging Euclidean Topological AutoencodersCode0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
Invertible Manifold Learning for Dimension ReductionCode0
Less is more: Faster and better music version identification with embedding distillationCode1
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins—0
Optimal High-order Tensor SVD via Tensor-Train Orthogonal IterationCode0
Parameter Optimization using high-dimensional Bayesian Optimization—0
Factorized Discriminant Analysis for Genetic Signatures of Neuronal PhenotypesCode0
Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler DivergenceCode0
Bayesian Feature Selection in Joint Quantile Time Series Analysis—0
Perplexity-free Parametric t-SNECode1
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Deep matrix factorizations—0
Evaluation of company investment value based on machine learning—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Parametric UMAP embeddings for representation and semi-supervised learningCode3
Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)—0
Deep Learning of Individual AestheticsCode1
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality ReductionCode1
Compact Learning for Multi-Label Classification—0
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and SurveyCode0
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying densityCode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data ProcessingCode0
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
PCA Reduced Gaussian Mixture Models with Applications in SuperresolutionCode0
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Sufficient Dimension Reduction for Average Causal Effect Estimation—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—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