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Dimensionality Reduction

89 papers with code · Computer Code

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

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Greatest papers with code

Adversarial Autoencoders

18 Nov 2015eriklindernoren/PyTorch-GAN

In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.

DIMENSIONALITY REDUCTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

9 Feb 2018lmcinnes/umap

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.

DIMENSIONALITY REDUCTION

t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

31 Jul 2018CannyLab/tsne-cuda

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.

DIMENSIONALITY REDUCTION

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

25 Dec 2017pavlin-policar/fastTSNE

t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years.

DIMENSIONALITY REDUCTION

Text Classification Algorithms: A Survey

17 Apr 2019kk7nc/Text_Classification

In this paper, a brief overview of text classification algorithms is discussed.

DIMENSIONALITY REDUCTION TEXT CLASSIFICATION

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

ICLR 2019 JustGlowing/minisom

We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.

DIMENSIONALITY REDUCTION REPRESENTATION LEARNING TIME SERIES

catch22: CAnonical Time-series CHaracteristics

29 Jan 2019benfulcher/hctsa

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.

DIMENSIONALITY REDUCTION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION

Accelerated Stochastic Power Iteration

10 Jul 2017noahgolmant/pytorch-hessian-eigenthings

We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity.

DIMENSIONALITY REDUCTION

Deep Continuous Clustering

ICLR 2018 shahsohil/DCC

We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly.

DIMENSIONALITY REDUCTION

Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

22 Oct 2014wielandbrendel/dPCA

Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables.

DECISION MAKING DIMENSIONALITY REDUCTION