.png){kind=icon}
Return
Handwritten digit classification using higher order singular value decomposition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2006.08.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5% even though the approximation reduces the original training data with more than 98% before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
handwritten digit classification
tensors
higher order singular value decomposition
tensor approximation
least squares
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveShareSaveRegio- and stereoselective synthesis of 1-aryl-1-thio-2-thiophosphinylethene derivatives via a radical process.png)
Tetrahedron
OAAI
TetrahedronIF0
OAAIShareSave no more
no more