Live briefing
Everything about Dependent component analysis
Signal separation method
Dependent component analysis (DCA) is a blind signal separation (BSS) method and an extension of Independent component analysis (ICA). ICA is the separating of mixed signals to individual signals without knowing anything about source signals. DCA is used to separate mixed signals into individual sets of signals that are dependent on signals within their own set, without knowing anything about the original signals. DCA can be ICA if all sets of signals only contain a single signal within their own set. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Dependent component analysisA 5-minute orientation
- Skim a recent paper: Reliability analysis for multi-component system considering failure propagation and dependent competing failure processInfluential work since 2022
- Pick up a classic book: Tax administrationThe most-reprinted book on the subject
- Read the landmark paper: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysisCited 23,985 times
- Go sideways: Independent component analysisA closely related topic
- Go sideways: Signal separationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
Reliability Analysis for Multi-Component Systems Subject to Multiple Dependent Competing Failure Processes
The two-component, ATP-dependent Clp protease of Escherichia coli. Purification, cloning, and mutational analysis of the ATP-binding component.
Least-dependent-component analysis based on mutual information
Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization
Reliability Analysis of Repairable Systems With Dependent Component Failures Under Partially Perfect Repair
Dependent masking and system life data analysis: Bayesian inference for two-component systems
Evaluation of Staining-Dependent Colour Changes in Resin Composites Using Principal Component Analysis
🆕 Influential since 2022
Reliability analysis for multi-component system considering failure propagation and dependent competing failure process
Reliability analysis of multi-component systems subjected to dependent degradation processes and random shocks in dynamic environments
Detecting Oceans on Exoplanets with Phase-dependent Spectral Principal Component Analysis
Protocol for semi-automatic EEG preprocessing incorporating independent component analysis and principal component analysis
Relativistic two-component time dependent density functional studies and Hirshfeld surface analysis of halogenated arylidenehydrazinylthiazole derivatives
📚 Books worth reading
- 📕Tax administrationUnited States. General Accounting Office · 1986
The web application hacker's handbookDafydd Stuttard, Marcus Pinto · 2007
Eloquent JavascriptMarijn Haverbeke · 2009
Noise and vibration analysisAnders Brandt · 2010
Modeling dyadic and interdependent data in the developmental and behavioral sciencesNoel A. Card, Todd D. Little · 2008
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Probabilistic transmission system planningWenyuan Li · 2011- 📕Comparing clinical measurement methodsBendix Carstensen · 2010
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Independent component analysis In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents.…
- Signal separation Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals,…
- Typical set In information theory, the typical set is a set of sequences whose probability is approximately 2−nH(X), where H(X) is the entropy of the source and n…
- Additive white Gaussian noise Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature…
- Rate–distortion theory Rate–distortion theory is a major branch of information theory which provides the theoretical foundations for lossy data compression; it addresses the…
- Wavelet packet decomposition Originally known as optimal subband tree structuring (SB-TS), also called wavelet packet decomposition (WPD; sometimes known as just wavelet packets or…
- Logistic regression In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent…
- Topological entropy In mathematics, the topological entropy of a topological dynamical system is a nonnegative extended real number that is a measure of the complexity of…