Title :
Generalized Locally Linear Embedding Based on Local Reconstruction Similarity
Author :
Zeng, Xianhua ; Luo, Siwei
Author_Institution :
Sch. of Comput., China West Normal Univ., Nanchong
Abstract :
Manifold learning has currently become a hot issue in the field of machine learning, pattern recognition and data mining. Locally linear embedding (LLE) is one of several promising manifold learning methods. But ordinary LLE can not distinguish effectively the low-dimensional embeddings of noise data. By introducing the reconstruction similarity into LLE, this paper proposes a generalized locally linear embedding algorithm based on local reconstruction similarity. Experimental results show on Columbia object image data that the new generalized version is superior to LLE in revealing the visualization of high-dimensional image dataset containing noise images.
Keywords :
data mining; data visualisation; learning (artificial intelligence); data mining; data visualization; generalized locally linear embedding; high-dimensional image dataset; local reconstruction similarity; machine learning; manifold learning; pattern recognition; Data mining; Data visualization; Image reconstruction; Learning systems; Machine learning; Machine learning algorithms; Manifolds; Nearest neighbor searches; Noise robustness; Pattern recognition; Local reconstruction similarity; Locally linear embedding (LLE); Manifold learning;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Jinan Shandong
Print_ISBN :
978-0-7695-3305-6
DOI :
10.1109/FSKD.2008.181