Dimensionality Reduction
722 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
Unsupervised speech representation learning using WaveNet autoencoders
We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features
FlorentF9/DeepTemporalClustering
•
•
Here we propose a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised.
Adapting Text Embeddings for Causal Inference
blei-lab/causal-text-embeddings
•
•
To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.
Unifying Deep Local and Global Features for Image Search
tensorflow/models
•
•
ECCV 2020
Image retrieval is the problem of searching an image database for items that are similar to a query image.
The Signature Kernel is the solution of a Goursat PDE
Recently, there has been an increased interest in the development of kernel methods for learning with sequential data.
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.
Efficient Manifold and Subspace Approximations with Spherelets
There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction.
Deep Continuous Clustering
We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly.
