Supervised dimensionality reduction
13 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Supervised dimensionality reduction
The vast majority of Dimensionality Reduction (DR) techniques rely on second-order statistics to define their optimization objective.
SRP: Efficient class-aware embedding learning for large-scale data via 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.
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.
The method finds the new classes close to the corresponding standard classes we took the data form.
We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient.
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.
Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science.
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item.