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

Separable Differential Equations01:20

Separable Differential Equations

202
A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...
202
Blind Procedures02:07

Blind Procedures

13.9K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.9K
Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

8.2K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
8.2K
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

2.1K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
2.1K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.4K
Independent and Dependent Sources01:18

Independent and Dependent Sources

2.9K
In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same author

Transcriptional repression by TGIF2 coordinates neurogenic priming and neural stem cell maintenance.

Science advances·2026
Same author

UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.

Nucleic acids research·2026
Same author

RegVelo: Gene-regulatory-informed dynamics of single cells.

Cell·2026
Same author

Glial multicellular programs reveal distinct patient stratification in Parkinson's disease.

Research square·2026
Same author

TarDis: Achieving robust and structured disentanglement of multiple covariates.

Cell systems·2026
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

976

A new concept for separability problems in blind source separation.

Fabian J Theis1

  • 1Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany. fabian@theis.name

Neural Computation
|July 22, 2004
PubMed
Summary
This summary is machine-generated.

Blind source separation (BSS) recovers independent sources from mixed signals. This study provides a simpler proof for linear BSS separability using Hessian diagonalization and proposes a new algorithm, extending to nonlinear models.

More Related Videos

A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

7.0K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.7K

Related Experiment Videos

Last Updated: Mar 24, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

976
A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

7.0K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.7K

Area of Science:

  • Signal Processing
  • Statistical Inference
  • Machine Learning

Background:

  • Blind Source Separation (BSS) aims to recover original independent sources from a mixed signal without prior knowledge of the mixing process.
  • Understanding the problem's indeterminacies, known as separability, is crucial for successful BSS.
  • Previous work established linear BSS separability using the Darmois-Skitovitch theorem.

Purpose of the Study:

  • To provide a simpler, direct proof for linear BSS separability.
  • To introduce a novel algorithm for BSS based on Hessian diagonalization.
  • To explore generalizations of separability to nonlinear BSS models.

Main Methods:

  • Utilizing the property that a random vector is independent if and only if the Hessian of its logarithmic density or characteristic function is everywhere diagonal.
  • Developing a new BSS algorithm leveraging this Hessian diagonalization property.
  • Investigating the extension of Hessian diagonalization to postnonlinear BSS.

Main Results:

  • A simplified and direct proof for linear BSS separability is presented.
  • A new algorithm for BSS is proposed based on the Hessian diagonalization principle.
  • Initial explorations into generalizing separability to nonlinear models are conducted.

Conclusions:

  • The Hessian diagonalization property offers a more straightforward approach to proving linear BSS separability.
  • The proposed algorithm provides a new method for performing BSS.
  • The study lays groundwork for extending these separability concepts to more complex nonlinear BSS scenarios.