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Subspace Learning Based on Tensor Analysis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cai, Deng He, Xiaofei Han, Jiawei |
| Copyright Year | 2005 |
| Abstract | Linear dimensionality reduction techniques have been widely used in pattern recognition and computer vision, such as face recognition, image retrieval, etc. The typical methods include Principal Component Analysis (PCA) which is unsupervised and Linear Discriminant Analysis (LDA) which is supervised. Both of them consider an m1 × m2 image as a high dimensional vector in Rm1×m2 . Such a vector representation fails to take into account the spatial locality of pixels in the image. An image is intrinsically a matrix. In this paper, we consider an image as the second order tensor in Rm1⊗Rm2 and propose two novel tensor subspace learning algorithms called TensorPCA and TensorLDA. Our algorithms explicitly take into account the relationship between column vectors of the image matrix and the relationship between the row vectors of the image matrix. We compare our proposed approaches with PCA and LDA for face recognition on three standard face databases. Experimental results show that tensor analysis achieves better recognition accuracy, while being much more efficient. ∗ The work was supported in part by the U.S. National Science Foundation NSF IIS-02-09199/IIS-03-08215. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.ideals.illinois.edu/bitstream/handle/2142/11025/Subspace%20Learning%20Based%20on%20Tensor%20Analysis.pdf?sequence=2 |
| Alternate Webpage(s) | http://www.cad.zju.edu.cn/home/dengcai/Publication/TR/UIUCDCS-R-2005-2572.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
