Supervised dimensionality reduction

13 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Adapting Text Embeddings for Causal Inference

blei-lab/causal-text-embeddings 29 May 2019

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 18 Jun 2017

The vast majority of Dimensionality Reduction (DR) techniques rely on second-order statistics to define their optimization objective.

Supervised Dimensionality Reduction for Big Data

neurodata/LOL 5 Sep 2017

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 7 Nov 2018

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

ZhenhuaShi/SDSPCAAN 9 Jan 2020

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

polavieja_lab/cnn-lda 22 Jun 2020

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

oozdenizci/MMIDimReduction 1 May 2021

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

SMART-Research/leaffeatures_paper 15 Jun 2021

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

molnsona/blossom 3 Jan 2022

Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science.

SLISEMAP: Supervised dimensionality reduction through local explanations

edahelsinki/slisemap 12 Jan 2022

Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item.