Supervised dimensionality reduction
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Supervised dimensionality reduction
Most implemented papers
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.
Dimensionality Reduction using Similarity-induced Embeddings
passalis/sef
•
•
The vast majority of Dimensionality Reduction (DR) techniques rely on second-order statistics to define their optimization objective.
Supervised Dimensionality Reduction for Big Data
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences.
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections
lightonai/supervised-random-projections
•
•
While stochastic approximation strategies have been explored for unsupervised dimensionality reduction to tackle this challenge, such approaches are not well-suited for accelerating computational speed for supervised dimensionality reduction.
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph.
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features
The method finds the new classes close to the corresponding standard classes we took the data form.
Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks
We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient.
Computer-aided Interpretable Features for Leaf Image Classification
The main image processing steps of our algorithm involves: i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove stalk, vi) Closing holes, and vii) Resize image.
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM
Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science.
SLISEMAP: Supervised dimensionality reduction through local explanations
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item.
