## Özet

Singular Value Decomposition (SVD) is a well studied research topic in many fields and applications from data mining to image processing. Data arising from these applications can be represented as a matrix where this matrix is large and sparse. Most existing algorithms are used to calculate singular values, left and right singular vectors of a largedense matrix but not large-sparse matrix. Even if they can find SVD of a large matrix, calculation of large-dense matrix has high time complexity due to sequential algorithms. Distributed approaches are proposed for computing SVD of large matrices. However, rank of the matrix is still being a problem when solving SVD with these distributed algorithms. In this paper we propose Ranky, set of methods to solve rank problem on large-sparse matrices in a distributed manner. Experimental results show that the Ranky approach recovers singular values, singular left and right vectors of a given large-sparse matrix with negligible error.

Orijinal dil | İngilizce |
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Ana bilgisayar yayını başlığı | 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 |

Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |

ISBN (Elektronik) | 9781538648360 |

DOI'lar | |

Yayın durumu | Yayınlandı - 15 Kas 2018 |

Etkinlik | 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 - Shah Alam, Malaysia Süre: 11 Tem 2018 → 12 Tem 2018 |

### Yayın serisi

Adı | 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 |
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???event.eventtypes.event.conference??? | 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 |
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Ülke/Bölge | Malaysia |

Şehir | Shah Alam |

Periyot | 11/07/18 → 12/07/18 |

### Bibliyografik not

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