메뉴 건너뛰기




Volumn , Issue , 2008, Pages 164-173

Identification of causal variables for building energy fault detection by semi-supervised LDA and decision boundary analysis

Author keywords

[No Author keywords available]

Indexed keywords

BUILDINGS; DISCRIMINANT ANALYSIS; ENERGY CONVERSION; KNOWLEDGE MANAGEMENT; TECHNICAL PRESENTATIONS;

EID: 62449332146     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2008.44     Document Type: Conference Paper
Times cited : (7)

References (27)
  • 1
    • 22844435720 scopus 로고    scopus 로고
    • SVM Decision Boundary Based Discriminative Subspace Induction
    • Oct
    • J. Zhang and Y. Liu. SVM Decision Boundary Based Discriminative Subspace Induction. Pattern Recognition, 38(10):1746-1758, Oct. 2005.
    • (2005) Pattern Recognition , vol.38 , Issue.10 , pp. 1746-1758
    • Zhang, J.1    Liu, Y.2
  • 9
    • 44649132677 scopus 로고    scopus 로고
    • A Unified Framework for Semi-supervised Dimensionality Reduction
    • Sep
    • Y. Song, F. Nie, C. Zhang, and S. Xiang. A Unified Framework for Semi-supervised Dimensionality Reduction. Pattern Recognition, 41(9):2789-2799, Sep. 2008.
    • (2008) Pattern Recognition , vol.41 , Issue.9 , pp. 2789-2799
    • Song, Y.1    Nie, F.2    Zhang, C.3    Xiang, S.4
  • 10
    • 44649160047 scopus 로고    scopus 로고
    • Semisupervised Local Fisher Discriminant Analysis for Dimensionality Reduction
    • Advances in Knowledge Discovery and Data Mining, Springer, Berlin
    • M. Sugiyama, T. Idé, S. Nakajima, and J. Sese. Semisupervised Local Fisher Discriminant Analysis for Dimensionality Reduction. Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, 5012:333-344, Springer, Berlin, 2008.
    • (2008) Lecture Notes in Computer Science , vol.5012 , pp. 333-344
    • Sugiyama, M.1    Idé, T.2    Nakajima, S.3    Sese, J.4
  • 15
    • 33750729556 scopus 로고    scopus 로고
    • Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
    • 7(Nov):2399-2434
    • M. Belkin, P. Niyogi, and V. Sindhwani. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research, 7(Nov):2399-2434, 2006.
    • (2006) Journal of Machine Learning Research
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 17
    • 84880203756 scopus 로고    scopus 로고
    • Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
    • MIT Press, Cambridge, MA
    • M. Belkin and P. Niyogi. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. Advances in Neural Information Processing Systems 14, MIT Press, Cambridge, MA, 2002.
    • (2002) Advances in Neural Information Processing Systems 14
    • Belkin, M.1    Niyogi, P.2
  • 20
    • 0034296402 scopus 로고    scopus 로고
    • Generalized Discriminant Analysis Using a Kernel Approach
    • Oct
    • G. Baudat and F. Anouar. Generalized Discriminant Analysis Using a Kernel Approach. Neural Information, 12(10):2385-2404, Oct. 2000.
    • (2000) Neural Information , vol.12 , Issue.10 , pp. 2385-2404
    • Baudat, G.1    Anouar, F.2
  • 21
    • 14544297033 scopus 로고    scopus 로고
    • KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition
    • Feb
    • J. Yang, A. F. Frangi, J. Yang, and D. Zhang. KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):230-244, Feb. 2005.
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.2 , pp. 230-244
    • Yang, J.1    Frangi, A.F.2    Yang, J.3    Zhang, D.4
  • 22
    • 33846432352 scopus 로고    scopus 로고
    • Identification of Contributing Variables Using Kernel-based Discriminant Modeling and Reconstruction
    • Aug
    • H.-W. Cho. Identification of Contributing Variables Using Kernel-based Discriminant Modeling and Reconstruction. Expert System with Applications, 33(2):274-285, Aug. 2007.
    • (2007) Expert System with Applications , vol.33 , Issue.2 , pp. 274-285
    • Cho, H.-W.1
  • 23
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear Component Analysis as a Kernel Eigenvalue Problem
    • Jul
    • B. Schölkopf, A, Smola, and K.-R. Müller. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10(5):1299-1319, Jul. 1998.
    • (1998) Neural Computation , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3
  • 24
    • 33750522220 scopus 로고    scopus 로고
    • Kernel PCA for Novelty Detection
    • Mar
    • H. Hoffmann. Kernel PCA for Novelty Detection. Pattern Recognition, 40(3):863-878, Mar. 2007.
    • (2007) Pattern Recognition , vol.40 , Issue.3 , pp. 863-878
    • Hoffmann, H.1
  • 27
    • 0000764772 scopus 로고
    • The Use of Multiple Measurements in Taxonomic Problems
    • R. A. Fisher. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7(II):179-188, 1936.
    • (1936) Annals of Eugenics , vol.7 , Issue.II , pp. 179-188
    • Fisher, R.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.