Skoči na glavni sadržaj

Tehnički vjesnik, Vol. 27 No. 4, 2020.

Izvorni znanstveni članak

https://doi.org/10.17559/TV-20190328105259

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

Hayat Ullah orcid id orcid.org/0000-0001-7579-2864 ; International Islamic University Islamabad (IIUI), DEE, FET, Sector H-10, 44000 Islamabad, Pakistan
Muhammad Amir ; International Islamic University Islamabad (IIUI), DEE, FET, Sector H-10, 44000 Islamabad, Pakistan
Muhammad Iqbal ; International Islamic University Islamabad (IIUI), DEE, FET, Sector H-10, 44000 Islamabad, Pakistan
Ahmad Khan orcid id orcid.org/0000-0002-6955-8876 ; COMSATS University Islamabad (CUI), Abbottabad Campus, Department of Computer Science, University Road, Tobe Camp, 22060 Abbottabad, Pakistan
Wasim Khan ; International Islamic University Islamabad (IIUI), DEE, FET, Sector H-10, 44000 Islamabad, Pakistan

Puni tekst: engleski pdf 1.083 Kb

str. 1174-1180

preuzimanja: 552

citiraj

Sažetak

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.

Ključne riječi

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

Hrčak ID:

242318

URI

https://hrcak.srce.hr/242318

Datum izdavanja:

15.8.2020.

Posjeta: 1.474 *