Yasuo Matsuyama

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Yasuo Matsuyama
Born (1947-03-23) March 23, 1947 (age 70)
Yokohama, Japan
Citizenship  Japan
Nationality Japanese
Fields Machine learning and human-aware information processing
Institutions Waseda University

Yasuo Matsuyama (born March 23, 1947) is a researcher in machine learning and human-aware information processing. He is a professor of Waseda University in the Department of Computer Science and Engineering where he teaches and studies machine learning, signal processing and computing methodologies with their applications to multimedia processing,[1][2] brain information processing, robotics[3] and bioinformatics. Human-aware information processing is his target.

Matsuyama is known for the founder of the α-expectation-maximization algorithm[4] which contains the expectation-maximization algorithm as its subset. The α-hidden Markov model estimation algorithm (extended Baum-Welch algorithm)[5] is also his invention. He is a co-inventor of the RapidICA (Rapid Independent Component Analysis).[6]

His early contributions include stochastic modeling of neural spike trains[7][8] and signal processing algorithms for data compression systems.[9][10][11]

Early life and education[edit]

Matsuyama graduated from Waseda University in Tokyo, obtaining his B. Engineering in Electrical Engineering in 1969, and his M. Engineering in 1971. In 1974 he received Dr. Engineering on “Stochastic modeling of neurons(advisors; Jun'ichi Takagi, Kageo Akizuki, and Katsuhiko Shirai).” Then, he got the Japan-US Exchange Fellowship. In 1978, he received his Ph.D. in Electrical Engineering at Stanford University on “Process distortion measures and signal processing (advisor; Robert M. Gray).”


From 1979 to 1996, he was a faculty of Ibaraki University, Japan (final position was a graduate school chairperson). Since 1996, Matsuyama is a Professor of Waseda University, Department of Computer Science and Engineering. From 2011, he is the director of the Media Network Center of Waseda University. At the 2011 Tōhoku earthquake and tsunami of March 2011, he was in charge of the safety inquiry of 65,000 students, staffs and faculties.

Matsuyama devised methods for fast hidden Markov model estimation, fast independent component analysis, decomposition of DNA sequences and light-weight systems for brain-humanoid interface (cf. Reference 3).

Awards and honors[edit]

  • 2008: Y. Dote Memorial Best Paper Award of CSTST'2008 from ACM and IEEE
  • 2006: LSI Intellectual Property Design Award from the LSI IP Committee
  • 2004: Best Paper Award for Application Oriented Research from Asia Pacific Neural Network Assembly
  • 2002: Fellow Award from IEICE
  • 2001: Telecommunication System Major Award from Telecommunication Promotion Foundation
  • 2001: Outstanding Paper Award of IEEE Transactions on Neural Networks
  • 1998: Fellow Award from IEEE
  • 1992: Best Paper Award from IEICE
  • 1989: Telecommunication System Promotion Award from Telecommunication Promotion Foundation


  1. ^ Matsuyama, Yasuo (1996). "Harmonic competition: A self-organizing multiple criteria optimization". IEEE Transactions on Neural Networks. IEEE Information Theory Society. 7: 652–668. doi:10.1109/72.501723. 
  2. ^ Matsuyama, Yasuo (1998). "Multiple descent cost competition: Restorable self-organization and multimedia information processing". IEEE Transactions on Neural Networks. IEEE Information Theory Society. 9: 106–122. doi:10.1109/72.655033. 
  3. ^ Matsuyama, Y.; Noguchi, K.; Hatakeyama, T.; Ochiai, N.; Hori, T. (2010). "Brain signal recognition and conversion towards symbiosis with ambulatory humanoids". Lecture Notes in Artificial Intelligence. Springer (6334): 101–111. doi:10.1007/978-3-642-15314-3_10. 
  4. ^ Matsuyama, Yasuo (2003). "The α-EM algorithm: Surrogate likelihood maximization using α-logarithmic information measures". IEEE Transactions on Information Theory. IEEE Information Theory Society. 49 (3): 692–706. doi:10.1109/tit.2002.808105. 
  5. ^ Matsuyama, Yasuo (2011). "Hidden Markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs". Proc. International Joint Conference on Neural Networks: 808–816. doi:10.1109/IJCNN.2011.6033304. 
  6. ^ Yokote, Ryota; Matsuyama, Yasuo (2010). "Yet rapid ICA: Applications to un-indexed image-to-image retrieval". Proc. International Joint Conference on Neural Networks: 4255–4262. doi:10.1109/IJCNN.2010.5596895. 
  7. ^ Matsuyama, Y.; Shirai, K.; Akizuki, K. (1974). "On some properties on stochastic information processes in neurons and neuron populations". Kybernetik (Biological Cybernetics). 15: 127–145. doi:10.1007/bf00274585. 
  8. ^ Matsuyama, Yasuo (1976). "A note on stochastic modeling of shunting inhibition". Biological Cybernetics. 24: 139–145. doi:10.1007/bf00364116. 
  9. ^ Gray, R. M.; Buzo, A.; Gray, A. H., Jr.; Matsuyama, Y. (1980). "Distortion measures for speech processing". IEEE Trans. on Acoustics, Speech and Signal Processing. ASSP-28: 367–376. 
  10. ^ Matsuyama, Y.; Gray, R. M. (1981). "Universal tree encoding for speech". IEEE Transactions on Information Theory. IEEE Information Theory Society. IT-27: 31–40. doi:10.1109/TIT.1981.1056306. 
  11. ^ Matsuyama, Y.; Gray, R. M. (1982). "Voice coding and tree encoding speech compression systems based upon inverse filter matching". IEEE Transactions on Communications. IEEE Information Theory Society. COM-30: 711–720. doi:10.1109/TCOM.1982.1095512. 

External links[edit]