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TIP

2010

2010

—We present in this paper a general formulation for nonnegative data factorization, called projective nonnegative graph embedding (PNGE), which 1) explicitly decomposes the data into two nonnegative components favoring the characteristics encoded by the so-called intrinsic and penalty graphs [31], respectively, and 2) explicitly describes how to transform each new testing sample into its low-dimensional nonnegative representation. In the past, such a nonnegative decomposition was often obtained for the training samples only, e.g., nonnegative matrix factorization (NMF) and its variants, nonnegative graph embedding (NGE) and its reﬁned version multiplicative nonnegative graph embedding (MNGE). Those conventional approaches for out-of-sample extension either suffer from the high computational cost or violate the basic nonnegative assumption. In this work, PNGE offers a uniﬁed solution to out-of-sample extension problem, and the nonnegative coefﬁcient vector of each datum is assum...

Related Content

Added |
31 Jan 2011 |

Updated |
31 Jan 2011 |

Type |
Journal |

Year |
2010 |

Where |
TIP |

Authors |
Xiaobai Liu, Shuicheng Yan, Hai Jin |

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