@inproceedings{4a3ac9a359d34ee68ca129445240f0e2,
title = "Multiple feature selection and fusion based on generalized N-dimensional independent component analysis",
abstract = "This paper proposes a framework of tensor-based ICA method for N-dimensional data analysis, which is called generalized N-dimensional ICA (GND-ICA). The proposed GND-ICA is based on multilinear algebra that treats N-dimensional data as a tensor without any unfolding preprocess. As an application, the GND-ICA can be used for multiple feature fusion and representation for color image classification. Multiple features extracted from a given image are constructed as a tensor. The effective components for each feature can be selected simultaneously and combined by the GND-ICA. This can obtain the improved classification results in comparison with various conventional linear and multilinear subspace learning methods.",
author = "Danni Ai and Guifang Duan and Xianhua Han and Chen, \{Yen Wei\}",
year = "2012",
language = "English",
isbn = "9784990644109",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "971--974",
booktitle = "ICPR 2012 - 21st International Conference on Pattern Recognition",
address = "United States",
note = "21st International Conference on Pattern Recognition, ICPR 2012 ; Conference date: 11-11-2012 Through 15-11-2012",
}