研究者
J-GLOBAL ID:201101061501008171   更新日: 2020年01月03日

Michael Houle

Michael Houle
所属機関・部署:
職名: 客員教授
ホームページURL (2件): http://kaken.nii.ac.jp/ja/r/90399270https://researchmap.jp/meh/
研究分野 (3件): 知能情報学 ,  数理情報学 ,  情報学基礎理論
研究キーワード (6件): data mining ,  similarity search ,  extreme value theory ,  algorithms ,  visualization ,  combinatorial geometry
競争的資金等の研究課題 (3件):
  • 2015 - 2018 Practical and Effective Data Mining via Local Intrinsic Dimensional Modeling
  • 2013 - 2017 超高次元データ空間における統計的推定・シミュレーション原理の開発と応用展開
  • 2012 - 2015 • ラベル伝播による画像データセットにおける顔への自動ラベル付け手法
論文 (105件):
  • Ruben Becker, Imane Hafnaoui, Michael E. Houle, Pan Li, Arthur Zimek. Subspace Determination Through Local Intrinsic Dimensional Decomposition. 12th International Conference on Similarity Search and Applications (SISAP 2019). 2019. 281-289
  • Brankica Bratić, Michael E. Houle, Vladimir Kurbalija, Vincent Oria, Miloš Radovanović. The Influence of Hubness on NN-Descent. International Journal on Artificial Intelligence Tools. 2019. 28. 6. 1960002:1-1960002:23
  • Yunzhe Jia, James Bailey, Kotagiri Ramamohanarao, Christopher Leckie, Michael E. Houle. Improving the Quality of Explanations with Local Embedding Perturbations. 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019). 2019. 875-884
  • Laurent Amsaleg, Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi, Miloš Radovanović, Weeris Treeratanajaru. Intrinsic Dimensionality Estimation within Tight Localities. 19th SIAM International Conference on Data Mining (SDM 2019). 2019. 181-189
  • Michael E. Houle, Vincent Oria, Kurt R. Rohloff, Arwa M. Wali. LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method. 11th International Conference on Similarity Search and Applications (SISAP 2018). 2018. 134-147
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MISC (14件):
  • Ruben Becker, Imane Hafnaoui, Michael E. Houle, Pan Li, Arthur Zimek. Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation. CoRR. 2019. abs/1907.06771. 1-17
  • Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani, Michael E. Houle, James Bailey. Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality. CoRR. 2019. abs/1905.00643. 1-31
  • Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Michael E. Houle, Grant Schoenebeck, Dawn Song, James Bailey. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. CoRR. 2018. abs/1801.02613. 1-15
  • Similarity Search and Applications - 9th International Conference, SISAP 2016, Tokyo, Japan, October 24-26, 2016. Springer Lecture Notes in Computer Science. 2016. 9939
  • On the Evaluation of Outlier Detection: Measures, Datasets, and an Empirical Study Continued. LWDA 2016 Workshop. 2016. 1-1
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特許 (5件):
  • Generating a data structure for information retrieval
  • Selection of elements strongly related to a predetermined reference element
  • Computer system, method, and program product for generating a data structure for information retrieval, and an associated graphical user interface
  • Information processing using a hierarchy structure of randomized samples
  • Computer executable dimension reduction and retrieval engine
書籍 (2件):
  • Data Mining: A Heuristic Approach Vol. I
    Idea Group Publishing 2002
  • Computing Handbook, 3rd ed.
    Chapman and Hall 2014
経歴 (10件):
  • 1989/09 - 1990/08 Kyushu University Research Associate
  • 1990/09 - 1992/04 University of Tokyo Research Associate
  • 1992/06 - 1997/12 University of Newcastle (Australia) Lecturer
  • 1998/01 - 1999/08 University of Newcastle (Australia) Senior Lecturer
  • 1999/09 - 2001/03 University of Sydney Senior Lecturer
全件表示
受賞 (3件):
  • 2018/06 - Best Paper Award, 8th International Conference on Web Intelligence, Mining and Semantics (WIMS 2018) NN-Descent on High-Dimensional Data
  • 2014/10 - 7th International Conference on Similarity Search and Applications (SISAP 2014) Best Paper Award Efficient Algorithms for Similarity Search in Axis-Aligned Subspaces
  • 2010/12 - 10th IEEE International Conference on Data Mining (ICDM 2010) Best Research Paper Award Finding Local Anomalies in Very High Dimensional Space
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