Dimensionality Reduction
726 papers with code • 0 benchmarks • 10 datasets
Dimensionality reduction is the task of reducing the dimensionality of a dataset.
( Image credit: openTSNE )
Benchmarks
These leaderboards are used to track progress in Dimensionality Reduction
Libraries
Use these libraries to find Dimensionality Reduction models and implementationsDatasets
CN-CELEB
Oxford105k
Oxford-Affine
AtariARI
GoodSounds
SoF
Deep Fakes Dataset
ALGAD
Most implemented papers
Reservoir computing approaches for representation and classification of multivariate time series
FilippoMB/Reservoir-model-space-classifier
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The architectures are compared to other MTS classifiers, including deep learning models and time series kernels.
Unsupervised Metric Learning in Presence of Missing Data
Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.
CatBoost: gradient boosting with categorical features support
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets.
catch22: CAnonical Time-series CHaracteristics
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.
Semantic Relatedness Based Re-ranker for Text Spotting
ahmedssabir/Semantic-Relatedness-Based-Reranker-for-Text-Spotting
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IJCNLP 2019
We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness.
Extracting the main trend in a dataset: the Sequencer algorithm
However, some are challenging to detect as they may be expressed in complex manners.
Improving the HardNet Descriptor
pultarmi/HardNet_MultiDataset
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In the thesis we consider the problem of local feature descriptor learning for wide baseline stereo focusing on the HardNet descriptor, which is close to state-of-the-art.
M-ar-K-Fast Independent Component Analysis
luca-parisi/m-arcsinh_scikit-learn_TensorFlow_Keras
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This study presents the m-arcsinh Kernel ('m-ar-K') Fast Independent Component Analysis ('FastICA') method ('m-ar-K-FastICA') for feature extraction.
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning
In this paper, we propose Wasserstein Isometric Mapping (Wassmap), a nonlinear dimensionality reduction technique that provides solutions to some drawbacks in existing global nonlinear dimensionality reduction algorithms in imaging applications.
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
