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Related Experiment Video

Updated: Oct 28, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks.

Ozan Özdenizci^1,2,3, Deniz Erdoğmuş¹

¹Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.

Information Sciences

|July 15, 2021

Summary

This summary is machine-generated.

This study introduces a novel neural network for feature selection, MMINet, using information theory to maximize class separability. It offers an end-to-end approach for dimensionality reduction in machine learning.

Keywords:

MMINet dimensionality reduction feature projection information theoretic learning mutual information neural networks stochastic gradient estimation

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Machine Learning
Information Theory
Computational Biology

Background:

Feature selection is crucial for supervised dimensionality reduction but can yield suboptimal results.
Existing methods may not optimize for class separability effectively.

Purpose of the Study:

To introduce an end-to-end neural network approach for feature transformation and selection.
To develop a method that maximizes mutual information between features and class labels for improved discriminative power.

Main Methods:

Developed a dimensionality reduction network (MMINet) utilizing the stochastic estimate of the mutual information gradient.
Formulated a non-parametric training objective without distributional assumptions.
Applied the method to high-dimensional biological datasets.

Main Results:

MMINet projects high-dimensional features into a lower-dimensional space maximizing mutual information with class labels.
The approach demonstrated effectiveness in experimental evaluations on biological data.
Conventional feature selection algorithms were shown to be a special case of this approach.

Conclusions:

Information theoretic feature transformation offers a powerful alternative to conventional feature selection.
MMINet provides an end-to-end trainable solution for discriminative dimensionality reduction.
The method shows promise for applications involving high-dimensional data, particularly in biology.