cobICA: A concentration-based, immune-inspired algorithm for ICA over Galois fields

Silva, Daniel G.; Montalvão, Jugurta; Attux, Romis

doi:10.1109/cimsivp.2014.7013284

Cited by 5 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…His ideas were analyzed and improved by Gutch et al [26]. In [21], Attux et al extended Yeredor's formulation for a more robust setup, in which the sources are not necessarily independent and presented a heuristic immune-inspired algorithm [21], which was later improved and generalized to any GF(q) [27].…”

Section: Previous Workmentioning

confidence: 99%

“…We verify that matrix B is not an identity, nor a permutation matrix, to make the experiment meaningful. We compare our suggested approach with three alternative methods: AMERICA, MEX-ICO (described in Section II) and cobICA [27]. As previously described, the cobICA is an immune-inspired algorithm.…”

Section: E Illustrationsmentioning

confidence: 99%

“…The cobICA algorithm requires a careful tuning of several parameters. Here, and in the following experiments, we follow the guidelines of the authors; we set the general parameters as appear in Table 1 of [27], while the rest of the parameters (concentration and suppression) are then optimized over a predefined set of values, in a preliminary independent experiment.…”

Section: E Illustrationsmentioning

confidence: 99%

“…In addition, we examine the block-wise approach, BloGLICA (Section III-D) with b = 2, 3. We compare our suggested bound and algorithms to cobICA [27]. Notice that in this experiment we omit the AMERICA and MEXICO algorithms, as they assume a generative mixture model, which is heavily violated in our setup.…”

Section: E Illustrationsmentioning

confidence: 99%

See 3 more Smart Citations

Linear Independent Component Analysis Over Finite Fields: Algorithms and Bounds

Painsky

Feder

2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Independent Component Analysis (ICA) is a statistical tool that decomposes an observed random vector into components that are as statistically independent as possible. ICA over finite fields is a special case of ICA, in which both the observations and the decomposed components take values over a finite alphabet. This problem is also known as minimal redundancy representation or factorial coding. In this work we focus on linear methods for ICA over finite fields. We introduce a basic lower bound which provides a fundamental limit to the ability of any linear solution to solve this problem. Based on this bound, we present a greedy algorithm that outperforms all currently known methods. Importantly, we show that the overhead of our suggested algorithm (compared with the lower bound) typically decreases, as the scale of the problem grows. In addition, we provide a sub-optimal variant of our suggested method that significantly reduces the computational complexity at a relatively small cost in performance. Finally, we discuss the universal abilities of linear transformations in decomposing random vectors, compared with existing non-linear solutions.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: E Illustrationsmentioning

confidence: 99%

Section: E Illustrationsmentioning

confidence: 99%

Section: E Illustrationsmentioning

confidence: 99%

See 2 more Smart Citations

Linear Independent Component Analysis Over Finite Fields: Algorithms and Bounds

Painsky

Feder

2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Since the search space size is proportional to q N 2 [26], there is a considerable increasing as compared to the space size of the first criterion, which is proportional to q N , thus hindering the use of exhaustive search methods in this case. Then, it is possible to consider again the application of populationbased metaheuristics such as AIS [27], [28], which offer signal separation with quality levels similar to exhaustive heuristics, but with a reduced computational cost. For instance, Figure 2 illustrates the successful application of the AIS-based method described in [28] for separation of black-and-white images.…”

Section: B Separation Over Gf (Q) In Instantaneous Modelsmentioning

confidence: 99%

Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

Silva

Duarte

Attux

2016

JCIS

Self Cite

View full text Add to dashboard Cite

The problem of blind source separation (BSS) has been intensively studied by the signal processing community. The first solutions to deal with BSS were proposed in the 1980's and are founded on the concept of independent component analysis (ICA). More recently, aiming at tackling some limitations of ICAbased methods, much attention has been paid to alternative BSS approaches. In this tutorial, in addition to providing a brief review of the classical BSS framework, we present two research trends in this area, namely source separation over Galois fields and sparse component analysis. For both subjects, we provide an overview of the main criteria, highlighting scenarios that can benefit from these more recent BSS paradigms.

show abstract

Simulated Annealing for Independent Component Analysis Over Galois Fields

Silva¹,

Attux

2018

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

cobICA: A concentration-based, immune-inspired algorithm for ICA over Galois fields

Cited by 5 publications

References 26 publications

Linear Independent Component Analysis Over Finite Fields: Algorithms and Bounds

Linear Independent Component Analysis Over Finite Fields: Algorithms and Bounds

Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

Simulated Annealing for Independent Component Analysis Over Galois Fields

Contact Info

Product

Resources

About