Using KCCA for Japanese-English cross-language information retrieval and classification

@inproceedings{Li1999UsingKF,
  title={Using KCCA for Japanese-English cross-language information retrieval and classification},
  author={Yaoyong Li and John Shawe-Taylor},
  year={1999}
}
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two multidimensional variables in feature space. We applied the KCCA to the Japanese-English cross-language information retrieval and classification. The results were encouraging. 
Highly Cited
This paper has 19 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-2 of 2 references

Relations between two sets of variates

H. Hotelling
Biometrika, 28:312–377, • 1936
View 1 Excerpt

Similar Papers

Loading similar papers…