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Amphicheirality of links and Alexander invariants
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作者 KADOKAMI Teruhisa KAWAUCHI Akio 《Science China Mathematics》 SCIE 2011年第10期2213-2227,共15页
We obtain an equation among invariants obtained from the Alexander module of an amphicheiral link. For special cases, it deduces necessary conditions on the Alexander polynomial. By using the present results and some ... We obtain an equation among invariants obtained from the Alexander module of an amphicheiral link. For special cases, it deduces necessary conditions on the Alexander polynomial. By using the present results and some known results, we show that the Alexander polynomial of an algebraically split component- preservingly (±)-amphicheiral link with even components is zero, and we determine prime amphieheiral links with at least 2 components and up to 9 crossings. 展开更多
关键词 amphicheiral link quadratic form signature invariants Alexander polynomial
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A novel texture clustering method based on shift invariant DWT and locality preserving projection
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作者 Rui XING San-yuan ZHANG Le-qing ZHU 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2009年第2期247-252,共6页
We propose a novel texture clustering method. A classical type of(approximate) shift invariant discrete wavelet transform(DWT),dual tree DWT,is used to decompose texture images. Multiple signatures are generated from ... We propose a novel texture clustering method. A classical type of(approximate) shift invariant discrete wavelet transform(DWT),dual tree DWT,is used to decompose texture images. Multiple signatures are generated from the obtained high-frequency bands. A locality preserving approach is applied subsequently to project data from high-dimensional space to low-dimensional space. Shift invariant DWT can represent image texture information efficiently in combination with a histogram signature,and the local geometrical structure of the dataset is preserved well during clustering. Experimental results show that the proposed method remarkably outperforms traditional ones. 展开更多
关键词 Shift invariant DWT. Texture signature Local preserving clustering Dimension reduction k-means
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