DocumentCode :
470502
Title :
Neural ISOMAP
Author :
Chao, Shih-Pin ; Yen, Chen-Lan ; Kuo, Chien-Chun
Author_Institution :
Ind. Technol. Res. Inst., Tainan
Volume :
1
fYear :
2007
fDate :
26-28 Nov. 2007
Firstpage :
333
Lastpage :
336
Abstract :
In recent years, the studies of digital content engineering confront us with massive amounts of data for classification and analysis, such as, thousands of news videos, surveillance records, motion capture data, images of animals and plants, etc. For these studies, the relationships between each data point are often hidden in a multi-dimensional space. For the reveal of the relationships between each data point, the ISOMAP method is often used. This is because that ISOMAP preserves the intrinsic dimensionality and metric structure of data. Therefore, this paper proposes a neural network-based ISOMAP method to efficiently obtain an ISOMAP robustly and stable. The benefits of the proposed method are that the time complexity is linear and space complexity is constant.
Keywords :
data analysis; neural nets; data analysis; data classification; digital content engineering; neural network-based ISOMAP method; Chaos; Data engineering; Euclidean distance; Fuzzy logic; Image analysis; Level measurement; Multidimensional systems; Neural networks; Principal component analysis; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-0-7695-2994-1
Type :
conf
DOI :
10.1109/IIH-MSP.2007.227
Filename :
4457557
Link To Document :
بازگشت