Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240100054-7.doi: 10.11896/jsjkx.240100054

• Big Data & Data Science • Previous Articles     Next Articles

Study on Linear Projection Method for Local Structure Adaptation

YANG Xing1,3, WANG Shitong2, HU Wenjun1,3   

  1. 1 School of Information Engineering,Huzhou University,Huzhou,Zhejiang 313000,China
    2 School of Artificial Intelligence and Computer,Jiangnan University,Wuxi,Jiangsu 214122,China
    3 Zhejiang Provincial Key Laboratory of Intelligent Management and Application of Modern Agricultural Resources,Huzhou,Zhejiang 313000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:YANG Xing,born in 1998,postgra-duate,is a member of CCF(No.N0566G).His main research interests is manifold learning.
    HU Wenjun,born in 1977,Ph.D,professor.His main research interests include machine learning and pattern recognition.
  • Supported by:
    Learning Theory,Key Learning Techniques and CASE Studies on Small-sample-sized Medical Imaging Data(U20A20228).

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Abstract: The core of manifold learning lies in capturing hidden geometric information within data by preserving local structures,which are typically assessed using redundant or noisy raw data.This implies that local structures are unreliable,giving rise to issues of insufficient confidence in local structures.To address this issue,a locally adaptive linear projection method is proposed.The essence of this method lies in two aspects:firstly,it enforces that the low-dimensional representation obtained through linear projection preserves local structures in the high-dimensional space;secondly,it updates the local structures in the high-dimensional space through the low-dimensional representation and achieves local structure adaptation through iterative cycles.Experimental results on real datasets demonstrate that the proposed method outperforms other comparative methods across various performance metrics.

Key words: Manifold learning, Local structure, Confidence, Adaptive, Linear projection

CLC Number: 

  • TP391
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