Independent Component Analysis

Hyvärinen, Aapo; Hurri, Jarmo; Hoyer, Patrik O.

doi:10.1007/978-1-84882-491-1_7

Cited by 508 publications

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“…We have to allow that some of them will not correspond with the true components but can be outliers caused by chemical noise or other types of imperfections that exist in experimental world. Hence, we propose information-theoretic criteria called negentropy, [15], to measure information content of to be estimated pure components and rank them according to estimated negentropy measure. Negentropy is differential entropy defined relatively to the entropy of the Gaussian process.…”

Section: ) Calculate Function F(a)mentioning

confidence: 99%

“…Provided that available number of linearly independent mixtures is equal or greater than the number of components, it is possible to separate mixture's spectra into component spectra using only the measurements of the mixture's spectra. This problem is generally known as blind source separation (BSS) and is for described case (more measured mixture's spectra than component spectra) solved by algorithms of independent component analysis (ICA), [11][12][13][14][15][16][17][18]. ICA assumes that pure components are statistically independent and that at most one is normally distributed.…”

Section: Introductionmentioning

confidence: 99%

“…ICA algorithms solve the BSS problem provided that source signals or pure components are statistically independent and non-Gaussian, as well as that NM, [14][15][16][17][18][19][20].…”

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confidence: 99%

“…with WA=PΛ, where W represents the de-mixing matrix, P is a general permutation matrix and Λ is a We recommend [15,16] for detailed description of how the ICA algorithms are constructed. Brief description of the ICA algorithms commonly used in analytical chemistry is also given in [12].…”

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confidence: 99%

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Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Kopriva

Jerić

Smrečki

2009

Analytica Chimica Acta

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Sparse Component Analysis (SCA) is demonstrated for blind extraction of three pure component spectra from only two measured mixed spectra in 13 C and 1 H nuclear magnetic resonance (NMR) spectroscopy. This appears to be the first time to report such results and that is the first novelty of the paper. Presented concept is general and directly applicable to experimental scenarios that possibly would require use of more than two mixtures. However, it is important to emphasize that number of required mixtures is always less than number of components present in these mixtures. The second novelty is formulation of blind NMR spectra decomposition exploiting sparseness of the pure components in the wavelet basis defined by either Morlet or Mexican hat wavelet. This enabled accurate estimation of the concentration matrix and number of pure components by means of data clustering algorithm and pure components § Patent pending under the number PCT/HR2008/000037. 2 spectra by means of linear programming with constraints from both 1 H and 13 C NMR experimental data.The third novelty is capability of proposed method to estimate number of pure components in demanding underdetermined blind source separation (uBSS) scenario. This is in contrast to majority of the BSS algorithms that assume this information to be known in advance. Presented results are important for the NMR spectroscopy-associated data analysis in pharmaceutical industry, medicine diagnostics and natural products research.

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Section: ) Calculate Function F(a)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…ICA algorithms solve the BSS problem provided that source signals or pure components are statistically independent and non-Gaussian, as well as that NM, [14][15][16][17][18][19][20].…”

mentioning

confidence: 99%

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confidence: 99%

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Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Kopriva

Jerić

Smrečki

2009

Analytica Chimica Acta

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“…For performing the dimensionality-reducing projection step from input representations I to the induced representation N, numerous methods such as multilayer perceptrons (MLP) [18], PCA [19], sparse coding [20], k-means and indeed, SOM [16] are available. Given our long-term goals for the PROPRE algorithm, the open-ended formation of representations as envisioned in Fig.…”

Section: Critical Examination and Justification Of Used Methodsmentioning

confidence: 99%

Efficient online bootstrapping of sensory representations

Gepperth

2013

Neural Networks

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This is a simulation-based contribution exploring a novel approach to the open-ended formation of multimodal representations in autonomous agents. In particular, we address the issue of transferring ("bootstrapping") feature selectivities between two modalities, from a previously learned or innate reference representation to a new induced representation. We demonstrate the potential of this algorithm by several experiments with synthetic inputs modeled after a robotics scenario where multimodal object representations are "bootstrapped" from a (reference) representation of object affordances. We focus on typical challenges in autonomous agents: absence of human supervision, changing environment statistics and limited computing power. We propose an autonomous and local neural learning algorithm termed PRO-PRE (projection-prediction) that updates induced representations based on predictability: competitive advantages are given to those feature-sensitive elements that are inferable from activities in the reference representation. PRO-PRE implements a bi-directional interaction of clustering ("projection") and inference ("prediction"), the key ingredient being an efficient online measure of predictability controlling learning in the projection step. We show that the proposed method is computationally efficient and stable, and that the multimodal transfer of feature selectivity is successful and robust under resource constraints. Furthermore, we successfully demonstrate robustness to noisy reference representations, non-stationary input statistics and uninformative inputs. to be an open-ended and largely unsupervised process, leading to internal representations of ever increasing specialization and usefulness to the agent. The labeled boxes indicate the work covered in this article: I 1 , I 2 , I 3 stand for input representations derived from sensors, N indicates the newly formed induced representation, and R stands for the reference representation that is derived from another sensory modality, controlling the bootstrapping process.

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Electromyographic (EMG) Decomposition

Marateb

McGill

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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An electromyographic (EMG) signal is a biomedical signal derived from measuring electrical potentials generated in contracting muscles. EMG signals can be recorded using either intramuscular (iEMG) or surface electrodes (sEMG). EMG signals are composed of motor unit action potential (MUAP) trains, the MUAP being the signal detected in response to the discharge of a group of muscle fibers innervated by a single motoneuron. The process of extracting the MUAP trains from an EMG signal is referred to as EMG decomposition. EMG decomposition makes it possible to study the behavior of individual motoneurons and thus to decode the neural drive to a muscle at a very precise level. This article reviews the different electrodes used to record iEMG and sEMG signals, the main steps in pattern‐matching decomposition algorithms (filtering, segmentation, clustering, classification, and resolving superpositions), the blind‐source separation techniques used for decomposing high‐density sEMG signals, the principal decomposition programs that have been described in the literature, including those that are publicly available, and approaches for assessing the accuracy of decomposition algorithms and particular decomposition results. Finally, an interactive tutorial on iEMG decomposition is provided.

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Independent Component Analysis

Cited by 508 publications

References 0 publications

Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Efficient online bootstrapping of sensory representations

Electromyographic (EMG) Decomposition

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