지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.10
- 수록면
- 777 - 782 (6page)
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초록· 키워드
오류제보하기
According to some flaws in the existing feature dimension reduction methods, a new method of two-step combined feature dimension reduction based on improved CHI algorithm and LSA algorithm is proposed in this paper. First, apply the improved CHI algorithm to realize the initial feature selection, resolve the problem of high dimension and sparseness in the feature space to a certain extent, and then use the LSA algorithm to extract the semantic structures in the initial feature space, and map it into the semantic feature space and realize the second dimension reduction. Experimental results indicate that this method of feature dimension reduction has a better performance, further improving the effect of topic tracking.
목차
Abstract
1. INTRODUCTION
2. FEATURE DIMENSION REDUCTION METHOD BASED ON EVALUATION FUNCTION
3. FEATURE DIMENSION REDUCTION METHOD BASED ON LSA ALGORITHM
4. FEATURE DIMENSION REDUCTION BASED ON THE IMPROVED CHI ALGORITHM AND LSA ALGORITHM
5. EXPERIMENTAL RESULTS AND ANALYSIS
6. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-569-000913549