Kompleks düzlemde büyük-ölçekli regresyon: Bilgilendirici olmayan verileri çevrimiçi olarak sansürleyen CRLS algoritmalarının başarım analizi

Büyük veri akışlarından anlamlı bilgilerin çıkarılması ve öğrenilmesi, toplumların yaşam kalitesinin artırılmasının, bilim ve mühendislik alanında yeni teknolojilerin geliştirilmesinin önünü açmaktadır. Öte yandan, sensör teknolojisindeki son atılımlar, hesaplama gücünün ve bilgisayar belleğinin artan kullanılabilirliği, verilerin sadece reel-değerli olmadığını artık büyük ölçekli kompleks-değerli veri kümeleriyle de başa çıkılması gerektiğini ortaya koymuştur. Bu amaç doğrultusunda, bu çalışmada, son zamanlarda önerilen çevrimiçi sansürleme (online censoring, OC) tabanlı kompleks-değerli özyinelemeli en küçük kareler (OC based complex-valued recursive least squares, OC-CRLS) ve OC tabanlı artırılmış CRLS (OC based augmented CRLS, OC-ACRLS) algoritmalarının başarımları ilk defa büyük ölçekli regresyon problemleri üzerinde detaylı olarak test edilmiş ve literatürde yer alan klasik versiyonları ile karşılaştırılmıştır. Benzetim çalışmaları, OC-CRLS ve OC-ACRLS algoritmalarının, OC mekanizmasının getirmiş olduğu avantajlardan dolayı kompleks düzlemde tanımlanmış olan büyük-ölçekli regresyon problemlerinde eğitim süresini ciddi anlamda kısalttığını ve test başarımını negatif yönde etkilemediğini göstermiştir. Bu da OC-CRLS ve OC-ACRLS algoritmalarının, kompleks düzlemde tanımlanabilen büyük veri akışı uygulamalarında etkin ve güçlü algoritmalar olduğunu kanıtlamıştır.

Large-scale regression in the complex domain: Performance analysis of CRLS algorithms censoring noninformative data in an online manner.

Extracting and learning meaningful information from big data streams paves the way for improving the quality of life of societies and the development of new technologies in the field of science and engineering. On the other hand, recent advances in sensor technology, increased availability of computing power and computer memory reveal that data is not just real-valued, but large-scale complex-valued datasets must also be dealt with. For this purpose, for the first time in this study, the performances of the recently proposed online censoring (OC) based complex-valued recursive least squares (OC-CRLS) and OC based augmented CRLS (OC-ACRLS) algorithms are tested on large-scale regression problems and compared with those of their classical versions in the literature in detail. Simulation studies show that the OC-CRLS and OC-ACRLS algorithms significantly shorten the training time in large-scale regression problems defined in the complex domain without affecting testing performance in a negative way, due to the advantages of their OC mechanism. This proves that OC-CRLS and OC-ACRLS algorithms are effective and powerful algorithms in big data streaming applications that can be defined in the complex domain.

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Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi-Cover
  • ISSN: 2564-6605
  • Yayın Aralığı: 4
  • Başlangıç: 2017
  • Yayıncı: Niğde Ömer Halisdemir Üniversitesi
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