Natural Gradient Approach to Independent Component Analysis

2003 ◽  
pp. 231-272 ◽  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Natural Gradient ◽  
Gradient Approach

An ICA Algorithm Based on Symmetric and Asymmetric Generalized Gaussian Model

2011 ◽  
Vol 204-210 ◽  
pp. 470-475
Author(s):  
Feng Zhao ◽  
Yun Jie Zhang ◽  
Min Cai
Keyword(s):  
Independent Component Analysis ◽  
Gradient Method ◽  
Gaussian Model ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Signal Separation ◽  
Natural Gradient ◽  
Analysis Model ◽  
Practical Applications ◽  
Source Signal

Maximum likelihood estimation is a very popular method to estimate the independent component analysis model because of good performance. Independent component analysis algorithm (the natural gradient method) based on this method is widely used in the field of blind signal separation. It potentially assumes that the source signal was symmetrical distribution, in fact in practical applications, source signals may be asymmetric. This article by distinguishing that the source signal is symmetrical or asymmetrical, proposes an improved natural gradient method based on symmetric generalized Gaussian model (People usually call generalized Gaussian model) and asymmetric generalized Gaussian model. The random mixed-signal simulation results show that the improved algorithm is better than the natural gradient separation method.

scholarly journals Tansformasi Lontar Babad Lombok Menuju Digitalisasi Berbasis Natural Gradient Flexible (NGF)

2021 ◽  
Vol 8 (2) ◽  
pp. 275
Author(s):  
Muhammad Tajuddin Anwar ◽  
Syahroni Hidayat ◽  
Ahmat Adil
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Visual Perspective ◽  
Natural Gradient ◽  
Main Challenge ◽  
Highly Correlated ◽  
Ica Algorithm ◽  
Lombok Island

<p class="Abstrak">Suku Sasak, yang tinggal di pulau Lombok Nusa Tenggara Barat, memiliki tradisi penulisan di daun lontar (<em>Borassus </em><em>Flabellifer</em>) kering, salah satunya adalah naskah Lontar Babad Lombok. Naskah Lontar Babad Lombok seiring berlalunya waktu, menjadi rapuh dan mudah patah sehingga memerlukan perawatan. Keadaan ini mendorongnya perlu dilakukan digitalisasi naskah lontar babad lombok sebagai bentuk pelestarian sehingga para generasi Milenial, khususnya di Lombok, dapat menikmati lontar babad lombok. Digitalisasi citra tersebut tantangan utama adalah tepi kabur teks dan perbedaan minimum antara teks dan bagian non-tekssebagai akibat dari proses perawatan. Oleh karena itu, dibutuhkan proses peningkatan kualitas citra hasil digitalisasi agar tulisan dapat lebih jelas terbaca. Salah satu metode yang terbukti mampu untuk memisahkan teks dari latar belakang yang sangat berkorelasi adalah <em>Natural Gradient Flexibel</em> (NGF) berbasiskan <em>Independent Component Analysis</em> (ICA), NGF-ICA. Penelitian ini bertujuan untuk melakukan peningkatan kualitas citra digitalisasi sebelum diumpankan pada database dan sistem informasi yang telah dibangun. Kualitas citra yang telah ditingkatkan diukur menggunakan metode MSE dan PSNR untuk tingkat kemiripannya, dan metode Entropi dan SSIM untuk informasi dan perspektif visual. Hasil penelitian menunjukkan bahwa penerapan algoritma NGF-ICA dapat memberikan citra keluaran dengan kualitas yang tinggi dengan nilai rata-rata MSE, PSNR, SSIM dan peningkatan Entropi sebesar 708, 19.95 db, 0.87 dan 0.45, secara berturut-turut.</p><p class="Abstrak"> </p><p><strong><em>Abstract</em></strong></p><p class="Abstract">Sasak tribe, who lives on Lombok Island, West Nusa Tenggara, has been writing manuscripts on dry palm leaves (Borassus Flabellifer) as a tradition, one of the manuscripts is Lontar Babad Lombok. As time pass by, the manuscript becomes brittle and breaks easily, therefore maintenances are required. this situation force the need to digitalize the manuscript as an act of preservation, hence the millennial generation, especially on Lombok Island, can enjoy the manuscript. the main challenge is the blurry edge of the text and the slight difference between the text and non-text part caused by the treatment process. Hence, it is needed to enhance the quality of the digitalize image to make the manuscript can be more clearly read. One of the proven methods that able to separate text from highly correlated backgrounds is Natural Gradient Flexibel (NGF) based on Independent Component Analysis (ICA), NGF-ICA. The aim of this study is to improve the quality of the digitized images before they fed into the database and information system that has been built. The enhanced image quality was measured, MSE and PSNR methods were used to measure the similarity level, and the Entropy and SSIM method were used to measure the information and visual perspective. The results show that the application of the NGF-ICA algorithm can generate high-quality output images with average values of MSE, PSNR, SSIM, and increasing Entropy by 708, 19.95 dB, 0.87, and 0.45, respectively.</p><p><strong><em><br /></em></strong></p>

Natural Gradient Learning for Over- and Under-Complete Bases in ICA

1999 ◽  
Vol 11 (8) ◽  
pp. 1875-1883 ◽  
Author(s):  
Shun-ichi Amari
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Learning Algorithm ◽  
Component Analysis ◽  
Independent Component ◽  
New Technique ◽  
Natural Gradient ◽  
Stiefel Manifolds ◽  
A New Technique ◽  
Gradient Learning

Independent component analysis or blind source separation is a new technique of extracting independent signals from mixtures. It is applicable even when the number of independent sources is unknown and is larger or smaller than the number of observed mixture signals. This article extends the natural gradient learning algorithm to be applicable to these overcomplete and undercomplete cases. Here, the observed signals are assumed to be whitened by preprocessing, so that we use the natural Riemannian gradient in Stiefel manifolds.

scholarly journals Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment

2007 ◽  
Vol 11 (10) ◽  
pp. 1197-1203
Author(s):  
Tuan A. Duong ◽  
◽  
Margaret A. Ryan ◽  
Vu A. Duong
Keyword(s):  
Independent Component Analysis ◽  
Gradient Descent ◽  
Sensor Array ◽  
Component Analysis ◽  
Independent Component ◽  
Sampled Data ◽  
Natural Gradient ◽  
Unknown Environment ◽  
Entropy Information ◽  
Time Invariant

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.

scholarly journals PEMISAHAN SINYAL BUNYI DARI MICROPHONE ARRAY DENGAN MENGGUNAKAN METODE BLIND SOURCE SEPARATION - INDEPENDENT COMPONENT ANALYSIS UNTUK MEMANTAU KONDISI POROS RETAK

2021 ◽  
Vol 2 ◽  
pp. 78-82
Author(s):  
Novitha L Th Thenu
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Time Domain ◽  
Microphone Array ◽  
Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Natural Gradient

Abstrak Tulisan ini mempresentasikan tentang pemisahan sinyal bunyi untuk memantau kondisi poros dengan menggunakan metode Blind Source Separation (BSS) - Independent Component Analysis (ICA). Pada penelitian ini, bunyi poros retak yang sementara berputar direkam melalui susunan mikrofon (microphone array) sebagai sensornya. Tiap-tiap mikrofon menerima sinyal dari poros tersebut, sehingga sinyal output dari tiap mikrofon merupakan sinyal campuran. BSS merupakan teknik memisahkan sinyal campuran berdasarkan analisa kebebasan statistik ICA sumber bunyi. Dengan memperhatikan jarak dan sudut datang antara mikrofon dengan poros maka tiap mikrofon menerima sinyal berbeda pula. Sinyal campuran dari tiap mikrofon akan diestimasi untuk memantau kondisi poros berdasarkan analisa pola bunyi. Pada penelitian ini pemisahan sinyal dilakukan pada time-domain dengan algoritma natural gradient. Berdasarkan hasil penelitian diperoleh metode pemisahan sinyal terbaik adalah metode pemisahan sinyal dalam kawasan waktu (TDICA) jauh lebih baik dari metode FDICA karena nilai MSE melalui TDICA jauh lebih kecil.

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