SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1625 of 3304 papers

TitleStatusHype
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Predicting Inflation with Recurrent Neural Networks—0
Deep Features for training Support Vector Machine—0
Adapting Speaker Embeddings for Speaker Diarisation—0
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word RepresentationsCode1
ProsoBeast Prosody Annotation ToolCode1
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers—0
Generative Locally Linear EmbeddingCode1
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Deep Learning of Conjugate MappingsCode0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Rethinking Spatial Dimensions of Vision TransformersCode1
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Model Order Reduction based on Runge-Kutta Neural Network—0
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Measuring and modeling the motor system with machine learning—0
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny SubspacesCode1
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—0
On the Whitney near extension problem, BMO, alignment of data, best approximation in algebraic geometry, manifold learning and their beautiful connections: A modern treatmentCode0
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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