Ito, Wasito
(2008)
*The extension of singular value decomposition with incomplete data.*
In:
Advances in image processing and pattern recognition: algorithms & practice, Vol. II.
Penerbit UTM, Johor, pp. 199-216.
ISBN 978-983-52-0618-4

Full text not available from this repository.

Official URL: http://www.penerbit.utm.my/bookchapterdoc/FSKSM/bo...

## Abstract

Feature extraction includes feature construction, space dimensionality reduction, sparse representations, and feature selection. All these techniques are commonly used as preprocessing to machine learning and statistics tasks of prediction, including pattern recognition and regression. Singular value decomposition is an important factorization of a rectangular matrix, with several applications in signal processing, object modeling and microarray data expressions analysis. In feature extraction application, the singular value decomposition mainly is used to find the projection of higher dimensionality data using principal component analysis (PCA). The SVD of a general matrix A is a transformation into a product of three matrices, each of which has a simple special form and geometric analysis.

Item Type: | Book Section |
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Uncontrolled Keywords: | principal component analysis (PCA), feature extraction |

Subjects: | Q Science > QA Mathematics > QA76 Computer software |

Divisions: | Computer Science and Information System (Formerly known) |

ID Code: | 27822 |

Deposited By: | Fazli Masari |

Deposited On: | 16 Aug 2012 08:11 |

Last Modified: | 05 Feb 2017 01:09 |

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