Accelerated Proximal Algorithm for Finding the Dantzig Selector and Source Separation Using Dictionary Learning

Tehnički Vjesnik

Ahmad Khan¹  Muhammad Iqbal²  Hayat Ullah²  Wasim Khan²  Muhammad Amir² 

[1] COMSATS University Islamabad (CUI), Abbottabad Campus, Department of Computer Science, University Road, Tobe Camp, 22060 Abbottabad, Pakistan;International Islamic University Islamabad (IIUI), DEE, FET, Sector H-10, 44000 Islamabad, Pakistan;

关键词: Accelerated Proximal Gradient Algorithm (APGA);    Alternating Direction Method of Multipliers (ADMM);    Dantzig Selector;    Electrocardiogram (ECG) signal;    Leukemia data;   

DOI  :  

来源: DOAJ

【 摘 要 】

In most of the applications, signals acquired from different sensors are composite and are corrupted by some noise. In the presence of noise, separation of composite signals into its components without losing information is quite challenging. Separation of signals becomes more difficult when only a few samples of the noisy undersampled composite signals are given. In this paper, we aim to find Dantzig selector with overcomplete dictionaries using Accelerated Proximal Gradient Algorithm (APGA) for recovery and separation of undersampled composite signals. We have successfully diagnosed leukemia disease using our model and compared it with Alternating Direction Method of Multipliers (ADMM). As a test case, we have also recovered Electrocardiogram (ECG) signal with great accuracy from its noisy version using this model along with Proximity Operator based Algorithm (POA) for comparison. With less computational complexity compared with ADMM and POA, APGA has a good clustering capability depicted from the leukemia diagnosis.

【 授权许可】

Unknown