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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 951–1000 of 3304 papers

TitleStatusHype
Dimension Reduction for Fréchet Regression—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
Stochastic neighborhood embedding and the gradient flow of relative entropy—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
A Wasserstein perspective of Vanilla GANs—0
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Dimension Reduction for time series with Variational AutoEncoders—0
An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection—0
Dimension reduction in recurrent networks by canonicalization—0
A framework for streamlined statistical prediction using topic models—0
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
Dimension Reduction Using Active Manifolds—0
Dimension Reduction via Colour Refinement—0
Dimension reduction via score ratio matching—0
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures—0
Classification of Cervical Cancer Dataset—0
Dimension Reduction with Non-degrading Generalization—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis—0
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction—0
Direction and Constraint in Phenotypic Evolution: Dimension Reduction and Global Proportionality in Phenotype Fluctuation and Responses—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders—0
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods—0
Discovery of sustainable energy materials via the machine-learned material space—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—0
Discriminative convolutional Fisher vector network for action recognition—0
Discriminative Dimension Reduction based on Mutual Information—0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español—0
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity—0
Disentangled Latent Spaces for Reduced Order Models using Deterministic Autoencoders—0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—0
Classic machine learning methods—0
Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks—0
Disentangling Topic Models: A Cross-cultural Analysis of Personal Values through Words—0
An Impossibility Theorem for Node Embedding—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