Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

39.9K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
39.9K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

34.9K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
34.9K
Aggregates Classification01:29

Aggregates Classification

571
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
571
Binomial Probability Distribution01:15

Binomial Probability Distribution

14.7K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
14.7K
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
Classification of Systems-II01:31

Classification of Systems-II

383
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
383

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Towards the construction of a virtual yeast.

Nature·2026
Same author

In silico design of novel precision vaccine targeting sclerostin epitopes for osteoporosis prevention and treatment.

Frontiers in immunology·2025
Same author

When to select two-level modified pedicle subtraction osteotomy in severe kyphosis secondary to ankylosing spondylitis?

BMC musculoskeletal disorders·2025
Same author

Human plasma metabolomics reveals metabolic targets for intervention in salt-sensitive hypertension.

Hypertension research : official journal of the Japanese Society of Hypertension·2025
Same author

FABP4 inhibition suppresses bone resorption and protects against postmenopausal osteoporosis in ovariectomized mice.

Nature communications·2025
Same author

The Geometry of Concepts: Sparse Autoencoder Feature Structure.

Entropy (Basel, Switzerland)·2025
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Pareto-Optimal Data Compression for Binary Classification Tasks.

Max Tegmark1, Tailin Wu1

  • 1Department of Physics, MIT Kavli Institute & Center for Brains, Minds & Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for lossy data compression, optimizing information retention for classification tasks. The approach maps data to a Pareto frontier, enabling information-theoretically optimal image clustering.

Keywords:
bottleneckclassificationcompressioninformation

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.1K

Related Experiment Videos

Last Updated: Nov 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.1K

Area of Science:

  • Information Theory
  • Machine Learning
  • Computer Vision

Background:

  • Lossy data compression aims to minimize storage costs while preserving essential information about specific attributes (Y) from a dataset (X).
  • This involves finding a mapping X → Z that maximizes mutual information I(Z, Y) under an entropy constraint H(Z).
  • Existing methods often struggle to efficiently map the trade-off between compression and information preservation.

Purpose of the Study:

  • To develop a novel method for mapping the Pareto frontier for classification tasks, balancing retained entropy and class information.
  • To present a technique for distilling data into a compressed representation that losslessly preserves class-discriminative information.
  • To generalize the discrete information bottleneck (DIB) problem and identify optimal compression points.

Main Methods:

  • A lossless mapping is proposed to distill data X from class Y into a lower-dimensional vector W, where I(W, Y) = I(X, Y).
  • For binary classification, W is further compressed into a discrete variable Z by binning, with parameter β controlling the compression level.
  • This process sweeps out the Pareto frontier, generalizing the DIB problem and identifying key 'corner' points.

Main Results:

  • The method successfully maps the Pareto frontier for classification, demonstrating the trade-off between compression and information.
  • Application to CIFAR-10, MNIST, and Fashion-MNIST datasets shows the approach acts as an information-theoretically optimal image clustering algorithm.
  • Pareto frontiers were found to be non-concave, and DIB phase transitions correspond to shifts between identified corner points.

Conclusions:

  • The proposed method provides an effective way to explore the information-theoretic limits of lossy compression for classification.
  • The identified 'corner' points offer a computationally efficient way to find optimal compression strategies without complex optimization.
  • The findings offer new insights into the behavior of DIB phase transitions and their relation to data clustering.