计算机科学 ›› 2012, Vol. 39 ›› Issue (4): 217-219.

• 人工智能 • 上一篇    下一篇

保持局部邻域关系的增量Hessian LLE算法

高翠珍,胡建龙,李德玉   

  1. (山西大学计算机与信息技术学院 太原030006)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Incremental Hessian LLE by Preserving Local Adjacent Information between Data Points

  • Online:2018-11-16 Published:2018-11-16

摘要: Hessian工工王算法是一种经典的流形学习算法,但该方法是以批处理的方式进行的,当新的数据点加入时,必须重新运行整个算法,计算所有数据点低维嵌入,原来的运算结果被全部丢弃。鉴于此,提出了一种保持局部部域关系的增量Hessian LLE(LIHLLE)算法,该方法通过保证流形新增样本点在原空间和嵌入空间局部部域的线性关系不变,用其已有邻域点的低维坐标线性表示新增样本点,来得到新增点的低维嵌入,实现增量学习。在Swiss roll withhole和frey_rawface数据集上的实验表明,该方法简便、有效可行。

关键词: 流形学习,Hcssian LLE,增量学习

Abstract: Hessian LLE algorithm is a classical manifold learning algorithm. However, Hessian LLE is a batch mode. If only new samples are observed, the whole algorithm must run repeatedly and all the former computational results are discarded. So,incremental Hessian LLE(LIHLLE) algorithm was proposed, which preserves local neighborhood relationship between the original space and the embedding space. New sample points were linearly reconstructed with existing embedding results of local neighborhood samples. The proposed method can learn manifold in an incremental way. Simulation results in Swiss roll with hole and frey_rawface database testify the efficiency and accuracy of the proposed algorithms.

Key words: Manifold learning, Hessian LLE, Incremental learning

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