Independent Subspaces

2011 ◽  
pp. 892-901 ◽  
Author(s):  
Lei Xu
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Signal Processing ◽  
Factor Analysis ◽  
Independent Component Analysis ◽  
General Framework ◽  
Principal Component ◽  
Component Analysis ◽  
The Third ◽  
Complicated Task

Several unsupervised learning topics have been extensively studied with wide applications for decades in the literatures of statistics, signal processing, and machine learning. The topics are mutually related and certain connections have been discussed partly, but still in need of a systematical overview. The article provides a unified perspective via a general framework of independent subspaces, with different topics featured by differences in choosing and combining three ingredients. Moreover, an overview is made via three streams of studies. One consists of those on the widely studied principal component analysis (PCA) and factor analysis (FA), featured by the second order independence. The second consists of studies on a higher order independence featured independent component analysis (ICA), binary FA, and nonGaussian FA. The third is called mixture based learning that combines individual jobs to fulfill a complicated task. Extensive literatures make it impossible to provide a complete review. Instead, we aim at sketching a roadmap for each stream with attentions on those topics missing in the existing surveys and textbooks, and limited to the authors’ knowledge.

scholarly journals Independent Component Analysis for IoT Services Using Machine Learning

2021 ◽  
pp. 309-316
Author(s):  
N NITHYANANDAM
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Principal Component ◽  
Evaluation Process ◽  
Component Analysis ◽  
Independent Component ◽  
Assessment Model ◽  
Machine Learning Model ◽  
Value Decomposition

Machine learning is an implementation of Artificial Intelligence (AI) that allows devices to learn and develop independently without having to be directly programmed. Machine learning is concerned with developing computer programmes that can access data and learn on their own. The introduction of the internet has revolutionised the use of machine learning in today's century. The new scheme, which seeks to create a quantifiable trust assessment model, then measures specific confidence qualities numerically. An epic calculation based on AI standards is conceived to portray the separated certainty highlights and join them to deliver a last trust characteristic to be utilized for dynamic. One of the methods used is the Generic Trust Computational Model (GTCM). It's a prototype that displays relevant details about the confidence acquisition and evaluation process using three Trust Metrics (TMs): experience, practise, and reputation. The Machine Learning Model uses the Principal Component Analysis (PCA) calculation, which depends on Singular Value Decomposition (SVD), to lessen the N measurements to two for perception purposes. In a number of implementations, Independent Component Analysis (ICA) has outperformed standard PCA. When Principal Component Analysis failed to differentiate eye artefacts from brain signals, particularly when their amplitudes were identical, it was used to exclude them from the ElectroEncephaloGram (EEG).

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Atmospheric radar signal processing using principal component analysis

2014 ◽  
Vol 32 ◽  
pp. 79-84 ◽  
Author(s):  
D. Uma Maheswara Rao ◽  
T. Sreenivasulu Reddy ◽  
G. Ramachandra Reddy
Keyword(s):  
Principal Component Analysis ◽  
Signal Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Radar Signal ◽  
Radar Signal Processing

PHARMACEUTICAL FORMULATION MANUFACTURER DISCRIMINATION BY INFRARED SPECTROSCOPY AND PRINCIPAL COMPONENT ANALYSIS

2019 ◽  
pp. 7-15
Author(s):  
E.M. Basova ◽  
Yu.N. Litvinenko ◽  
N.А. Polotnyanko
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
The Third ◽  
Chemometric Technique ◽  
Tablet Formulations

In the present work Fournier transform infrared (IR) spectroscopy in association with chemometric technique was employed to identify kind of tablet formulations containing paracetamol and/or caffeine as active pharmaceutical ingredients. 13 samples of 5 commercially available brand tablets of different manufacturers and batches were bayed in local pharmacies. IR spectra of samples were recorded in the range 600—4000 cm-1 and subjected to and principal component analysis (PCA) which allowed to clearly identify 5 clusters in the scores plot using the third and the second principal components, corresponding to the brands of tablets. For Paracetamol and Caffeine-sodium benzoate tablets the combination of IR spectroscopy and PCA was able to recognize the manufacturer on the basis of distance between samples in clusters in the PCA scores plot.

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