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Yasuo Matsuyama

April 14, 2024

Yasuo Matsuyama (born March 23, 1947) is a Japanese researcher in machine learning and human-aware information processing.

Yasuo Matsuyama
At the 2017 HPC Connection Workshop
Born (1947-03-23) March 23, 1947 (age 77)
Yokohama, Japan
NationalityJapanese
Alma materWaseda University (Dr. Engineering, 1974) Stanford University (PhD, 1978)
Known forAlpha-EM algorithm
Scientific career
FieldsMachine learning and human-aware information processing
InstitutionsWaseda University, Stanford University
Thesis Studies on Stochastic Modeling of Neurons (Dr. Engineering from Waseda University) . Process Distortion Measures and Signal Processing (PhD from Stanford University).
Doctoral advisorWaseda University: Jun'ichi Takagi, Kageo Akizuki, and Kastuhiko Shirai for Dr. Engineering Stanford University: Robert M. Gray for PhD
Websitehttp://www.f.waseda.jp/yasuo2/en/index.html

Matsuyama is a Professor Emeritus and an Honorary Researcher of the Research Institute of Science and Engineering of Waseda University.

Early life and education edit

Matsuyama received his bachelor’s, master’s and doctoral degrees in electrical engineering from Waseda University in 1969, 1971, and 1974 respectively. The dissertation title for the Doctor of Engineering is Studies on Stochastic Modeling of Neurons.[1] There, he contributed to the spiking neurons with stochastic pulse-frequency modulation. Advisors were Jun’ichi Takagi, Kageo, Akizuki, and Katsuhiko Shirai.

Upon the completion of the doctoral work at Waseda University, he was dispatched to the United States as a Japan-U.S. exchange fellow by the joint program of the Japan Society for the Promotion of Science, Fulbright Program, and the Institute of International Education. Through this exchange program, he completed his Ph.D. program at Stanford University in 1978. The dissertation title is Process Distortion Measures and Signal Processing.[2] There, he contributed to the theory of probabilistic distortion measures and its applications to speech encoding with spectral clustering or vector quantization. His advisor was Robert. M. Gray.

Career edit

From 1977 to 1078, Matsuyama was a research assistant at the Information Systems Laboratory of Stanford University.

From 1979 to 1996, he was a faculty of Ibaraki University, Japan (the final position was a professor and chairperson of the Information and System Sciences Major).

Since 1996, he was a Professor of Waseda University, Department of Computer Science and Engineering. From 2011 to 2013, he was the director of the Media Network Center of Waseda University. At the 2011 Tōhoku earthquake and tsunami of March 11, 2011, he was in charge of the safety inquiry of 65,000 students, staffs and faculties.

Since 2017, Matsuyama is a Professor Emeritus and an Honorary Researcher of the Research Institute of Science and Engineering of Waseda University. Since 2018, he serves as an acting president of the Waseda Electrical Engineering Society.

Work edit

Matsuyama’s works on machine learning and human-aware information processing have dual foundations. Studies on the competitive learning (vector quantization) for his Ph.D. at Stanford University brought about his succeeding works on machine learning contributions. Studies on stochastic spiking neurons[3][4] for his Dr. Engineering at Waseda University set off applications of biological signals to the machine learning. Thus, his works can be grouped reflecting these dual foundations.

Statistical machine learning algorithms: The use of the alpha-logarithmic likelihood ratio in learning cycles generated the alpha-EM algorithm (alpha-Expectation maximization algorithm).[5] Because the alpha-logarithm includes the usual logarithm, the alpha-EM algorithm contains the EM-algorithm (more precisely, the log-EM algorithm). The merit of the speedup by the alpha-EM over the log-EM is due to the ability to utilize the past information. Such a usage of the messages from the past brought about the alpha-HMM estimation algorithm (alpha-hidden Markov model estimation algorithm)[6] that is a generalized and faster version of the hidden Markov model estimation algorithm (HMM estimation algorithm).

Competitive learning on empirical data: Starting from the speech compression studies at Stanford, Matsuyama developed generalized competitive learning algorithms; the harmonic competition[7] and the multiple descent cost competition.[8] The former realizes the multiple-object optimization. The latter admits deformable centroids. Both algorithms generalize the batch-mode vector quantization (simply called, vector quantization) and the successive-mode vector quantization (or, called learning vector quantization).

A hierarchy from the alpha-EM to the vector quantization: Matsuyama contributed to generate and identify the hierarchy of the above algorithms.

On the class of the vector quantization and competitive learning, he contributed to generate and identify the hierarchy of VQs.

  • VQ ⇔ {batch mode VQ, and learning VQ}[8] ⊂ {harmonic competition}[7] ⊂ {multiple descent cost competition}.[8]

Applications to Human-aware information processing: The dual foundations of his led to the applications to huma-aware information processing.

  1. Retrieval systems for similar images[9] and videos.[10]
  2. Bipedal humanoid operations via invasive and noninvasive brain signals as well as gestures.[11]
  3. Continuous authentication of uses by brain signals.[12]
  4. Self-organization[7] and emotional feature injection based on the competitive learning.[8]
  5. Decomposition of DNA sequences by the independent component analysis (US Patent: US 8,244,474 B2).
  6. Data compression of speech signals by the competitive learning.[13][14][15]

The above theories and applications work as contributions to IoCT (Internet of Collaborative Things) and IoXT (http://www.asc-events.org/ASC17/Workshop.php).

Awards and honors edit

  • 2016: e-Teaching Award of Waseda University
  • 2015: Best Textbook Award by the Japanese Society of Information Processing
  • 2014: Fellow of the Japanese Society of Information Processing
  • 2013: IEEE Life Fellow
  • 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 the Institute of Electronics, Information and Communication Engineers.
  • 2001: Telecommunication System Major Award of the Telecommunications Advancement Foundation
  • 2001: Outstanding Paper Award of IEEE Transactions on Neural Networks 2013-01-17 at the Wayback Machine
  • 1998: Fellow Award from IEEE for contributions to learning algorithms with competition.[16]
  • 1992: Best Paper Award from the Institute of Electronics, Information and Communication Engineers
  • 1989: Telecommunication System Promotion Award of the Telecommunications Advancement Foundation

References edit

  1. ^ Matsuyama, Yasuo (1974-03). "Studies on Stochastic Modeling of neurons", http://www.f.waseda.jp/yasuo2/MatsuyamaWasedaDissertation.pdf
  2. ^ Matsuyama, Yasuo (1978-08). "Process Distortion Measures and Signal Processing", http://www.f.waseda.jp/yasuo2/MatsuyamaStanfordDissertation.pdf
  3. ^ Matsuyama, Yasuo; Shirai, Katsuhiko; Akizuki, Kageo (1974-09-01). "On some properties of stochastic information processes in neurons and neuron populations". Kybernetik. 15 (3): 127–145. doi:10.1007/BF00274585. ISSN 0023-5946. PMID 4853437. S2CID 31189652.
  4. ^ Matsuyama, Y. (1976-09-01). "A note on stochastic modeling of shunting inhibition". Biological Cybernetics. 24 (3): 139–145. doi:10.1007/BF00364116. ISSN 0340-1200. PMID 999955. S2CID 5211589.
  5. ^ a b Matsuyama, Y. (March 2003). "The alpha;-EM algorithm: surrogate likelihood maximization using alpha;-logarithmic information measures". IEEE Transactions on Information Theory. 49 (3): 692–706. doi:10.1109/tit.2002.808105. ISSN 0018-9448.
  6. ^ Matsuyama, Y. (July 2017). "The Alpha-HMM Estimation Algorithm: Prior Cycle Guides Fast Paths". IEEE Transactions on Signal Processing. 65 (13): 3446–3461. Bibcode:2017ITSP...65.3446M. doi:10.1109/tsp.2017.2692724. ISSN 1053-587X. S2CID 34883770.
  7. ^ a b c Matsuyama, Y. (May 1996). "Harmonic competition: a self-organizing multiple criteria optimization". IEEE Transactions on Neural Networks. 7 (3): 652–668. doi:10.1109/72.501723. ISSN 1045-9227. PMID 18263462.
  8. ^ a b c d Matsuyama, Y. (January 1998). "Multiple descent cost competition: restorable self-organization and multimedia information processing". IEEE Transactions on Neural Networks. 9 (1): 106–122. doi:10.1109/72.655033. ISSN 1045-9227. PMID 18252433.
  9. ^ Katsumata, Naoto; Matsuyama, Yasuo (2005). "Database retrieval for similar images using ICA and PCA bases". Engineering Applications of Artificial Intelligence. 18 (6): 705–717. doi:10.1016/j.engappai.2005.01.002.
  10. ^ Horie, Teruki; Shikano, Akihiro; Iwase, Hiromichi; Matsuyama, Yasuo (2015-11-09). "Learning Algorithms and Frame Signatures for Video Similarity Ranking". Neural Information Processing. Lecture Notes in Computer Science. Vol. 9489. Springer, Cham. pp. 147–157. doi:10.1007/978-3-319-26532-2_17. ISBN 9783319265315.
  11. ^ Matsuyama, Yasuo; Noguchi, Keita; Hatakeyama, Takashi; Ochiai, Nimiko; Hori, Tatsuro (2010-08-28). "Brain Signal Recognition and Conversion towards Symbiosis with Ambulatory Humanoids". Brain Informatics. Lecture Notes in Computer Science. Vol. 6334. Springer, Berlin, Heidelberg. pp. 101–111. doi:10.1007/978-3-642-15314-3_10. ISBN 9783642153136.
  12. ^ Matsuyama, Yasuo; Shozawa, Michitaro; Yokote, Ryota (2015). "Brain signal׳s low-frequency fits the continuous authentication". Neurocomputing. 164: 137–143. doi:10.1016/j.neucom.2014.08.084.
  13. ^ Gray, R.; Buzo, A.; Gray, A.; Matsuyama, Y. (August 1980). "Distortion measures for speech processing". IEEE Transactions on Acoustics, Speech, and Signal Processing. 28 (4): 367–376. doi:10.1109/tassp.1980.1163421. ISSN 0096-3518.
  14. ^ Matsuyama, Y.; Gray, R. (January 1981). "Universal tree encoding for speech". IEEE Transactions on Information Theory. 27 (1): 31–40. doi:10.1109/tit.1981.1056306. ISSN 0018-9448.
  15. ^ Matsuyama, Y.; Gray, R. (April 1982). "Voice Coding and Tree Encoding Speech Compression Systems Based Upon Inverse Filter Matching". IEEE Transactions on Communications. 30 (4): 711–720. doi:10.1109/tcom.1982.1095512. ISSN 0090-6778.
  16. ^ "IEEE Fellows 1998 | IEEE Communications Society".

External links edit

  • Yasuo Matsuyama, Professor Emeritus of Waseda University
  • at IEEE

April 14, 2024
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Yasuo MatsuyamaAt the 2017 HPC Connection WorkshopBorn 1947 03 23 March 23 1947 age 77 Yokohama JapanNationalityJapaneseAlma materWaseda University Dr Engineering 1974 Stanford University PhD 1978 Known forAlpha EM algorithmScientific careerFieldsMachine learning and human aware information processingInstitutionsWaseda University Stanford UniversityThesisStudies on Stochastic Modeling of Neurons Dr Engineering from Waseda University Process Distortion Measures and Signal Processing PhD from Stanford University Doctoral advisorWaseda University Jun ichi Takagi Kageo Akizuki and Kastuhiko Shirai for Dr Engineering Stanford University Robert M Gray for PhDWebsitehttp www f waseda jp yasuo2 en index htmlMatsuyama is a Professor Emeritus and an Honorary Researcher of the Research Institute of Science and Engineering of Waseda University Contents 1 Early life and education 2 Career 3 Work 4 Awards and honors 5 References 6 External linksEarly life and education editMatsuyama received his bachelor s master s and doctoral degrees in electrical engineering from Waseda University in 1969 1971 and 1974 respectively The dissertation title for the Doctor of Engineering is Studies on Stochastic Modeling of Neurons 1 There he contributed to the spiking neurons with stochastic pulse frequency modulation Advisors were Jun ichi Takagi Kageo Akizuki and Katsuhiko Shirai Upon the completion of the doctoral work at Waseda University he was dispatched to the United States as a Japan U S exchange fellow by the joint program of the Japan Society for the Promotion of Science Fulbright Program and the Institute of International Education Through this exchange program he completed his Ph D program at Stanford University in 1978 The dissertation title is Process Distortion Measures and Signal Processing 2 There he contributed to the theory of probabilistic distortion measures and its applications to speech encoding with spectral clustering or vector quantization His advisor was Robert M Gray Career editFrom 1977 to 1078 Matsuyama was a research assistant at the Information Systems Laboratory of Stanford University From 1979 to 1996 he was a faculty of Ibaraki University Japan the final position was a professor and chairperson of the Information and System Sciences Major Since 1996 he was a Professor of Waseda University Department of Computer Science and Engineering From 2011 to 2013 he was the director of the Media Network Center of Waseda University At the 2011 Tōhoku earthquake and tsunami of March 11 2011 he was in charge of the safety inquiry of 65 000 students staffs and faculties Since 2017 Matsuyama is a Professor Emeritus and an Honorary Researcher of the Research Institute of Science and Engineering of Waseda University Since 2018 he serves as an acting president of the Waseda Electrical Engineering Society Work editMatsuyama s works on machine learning and human aware information processing have dual foundations Studies on the competitive learning vector quantization for his Ph D at Stanford University brought about his succeeding works on machine learning contributions Studies on stochastic spiking neurons 3 4 for his Dr Engineering at Waseda University set off applications of biological signals to the machine learning Thus his works can be grouped reflecting these dual foundations Statistical machine learning algorithms The use of the alpha logarithmic likelihood ratio in learning cycles generated the alpha EM algorithm alpha Expectation maximization algorithm 5 Because the alpha logarithm includes the usual logarithm the alpha EM algorithm contains the EM algorithm more precisely the log EM algorithm The merit of the speedup by the alpha EM over the log EM is due to the ability to utilize the past information Such a usage of the messages from the past brought about the alpha HMM estimation algorithm alpha hidden Markov model estimation algorithm 6 that is a generalized and faster version of the hidden Markov model estimation algorithm HMM estimation algorithm Competitive learning on empirical data Starting from the speech compression studies at Stanford Matsuyama developed generalized competitive learning algorithms the harmonic competition 7 and the multiple descent cost competition 8 The former realizes the multiple object optimization The latter admits deformable centroids Both algorithms generalize the batch mode vector quantization simply called vector quantization and the successive mode vector quantization or called learning vector quantization A hierarchy from the alpha EM to the vector quantization Matsuyama contributed to generate and identify the hierarchy of the above algorithms Alpha EM 5 log EM basic competitive learning vector quantization VQ or clustering On the class of the vector quantization and competitive learning he contributed to generate and identify the hierarchy of VQs VQ batch mode VQ and learning VQ 8 harmonic competition 7 multiple descent cost competition 8 Applications to Human aware information processing The dual foundations of his led to the applications to huma aware information processing Retrieval systems for similar images 9 and videos 10 Bipedal humanoid operations via invasive and noninvasive brain signals as well as gestures 11 Continuous authentication of uses by brain signals 12 Self organization 7 and emotional feature injection based on the competitive learning 8 Decomposition of DNA sequences by the independent component analysis US Patent US 8 244 474 B2 Data compression of speech signals by the competitive learning 13 14 15 The above theories and applications work as contributions to IoCT Internet of Collaborative Things and IoXT http www asc events org ASC17 Workshop php Awards and honors edit2016 e Teaching Award of Waseda University 2015 Best Textbook Award by the Japanese Society of Information Processing 2014 Fellow of the Japanese Society of Information Processing 2013 IEEE Life Fellow 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 the Institute of Electronics Information and Communication Engineers 2001 Telecommunication System Major Award of the Telecommunications Advancement Foundation 2001 Outstanding Paper Award of IEEE Transactions on Neural Networks Archived 2013 01 17 at the Wayback Machine 1998 Fellow Award from IEEE for contributions to learning algorithms with competition 16 1992 Best Paper Award from the Institute of Electronics Information and Communication Engineers 1989 Telecommunication System Promotion Award of the Telecommunications Advancement FoundationReferences edit Matsuyama Yasuo 1974 03 Studies on Stochastic Modeling of neurons http www f waseda jp yasuo2 MatsuyamaWasedaDissertation pdf Matsuyama Yasuo 1978 08 Process Distortion Measures and Signal Processing http www f waseda jp yasuo2 MatsuyamaStanfordDissertation pdf Matsuyama Yasuo Shirai Katsuhiko Akizuki Kageo 1974 09 01 On some properties of stochastic information processes in neurons and neuron populations Kybernetik 15 3 127 145 doi 10 1007 BF00274585 ISSN 0023 5946 PMID 4853437 S2CID 31189652 Matsuyama Y 1976 09 01 A note on stochastic modeling of shunting inhibition Biological Cybernetics 24 3 139 145 doi 10 1007 BF00364116 ISSN 0340 1200 PMID 999955 S2CID 5211589 a b Matsuyama Y March 2003 The alpha EM algorithm surrogate likelihood maximization using alpha logarithmic information measures IEEE Transactions on Information Theory 49 3 692 706 doi 10 1109 tit 2002 808105 ISSN 0018 9448 Matsuyama Y July 2017 The Alpha HMM Estimation Algorithm Prior Cycle Guides Fast Paths IEEE Transactions on Signal Processing 65 13 3446 3461 Bibcode 2017ITSP 65 3446M doi 10 1109 tsp 2017 2692724 ISSN 1053 587X S2CID 34883770 a b c Matsuyama Y May 1996 Harmonic competition a self organizing multiple criteria optimization IEEE Transactions on Neural Networks 7 3 652 668 doi 10 1109 72 501723 ISSN 1045 9227 PMID 18263462 a b c d Matsuyama Y January 1998 Multiple descent cost competition restorable self organization and multimedia information processing IEEE Transactions on Neural Networks 9 1 106 122 doi 10 1109 72 655033 ISSN 1045 9227 PMID 18252433 Katsumata Naoto Matsuyama Yasuo 2005 Database retrieval for similar images using ICA and PCA bases Engineering Applications of Artificial Intelligence 18 6 705 717 doi 10 1016 j engappai 2005 01 002 Horie Teruki Shikano Akihiro Iwase Hiromichi Matsuyama Yasuo 2015 11 09 Learning Algorithms and Frame Signatures for Video Similarity Ranking Neural Information Processing Lecture Notes in Computer Science Vol 9489 Springer Cham pp 147 157 doi 10 1007 978 3 319 26532 2 17 ISBN 9783319265315 Matsuyama Yasuo Noguchi Keita Hatakeyama Takashi Ochiai Nimiko Hori Tatsuro 2010 08 28 Brain Signal Recognition and Conversion towards Symbiosis with Ambulatory Humanoids Brain Informatics Lecture Notes in Computer Science Vol 6334 Springer Berlin Heidelberg pp 101 111 doi 10 1007 978 3 642 15314 3 10 ISBN 9783642153136 Matsuyama Yasuo Shozawa Michitaro Yokote Ryota 2015 Brain signal s low frequency fits the continuous authentication Neurocomputing 164 137 143 doi 10 1016 j neucom 2014 08 084 Gray R Buzo A Gray A Matsuyama Y August 1980 Distortion measures for speech processing IEEE Transactions on Acoustics Speech and Signal Processing 28 4 367 376 doi 10 1109 tassp 1980 1163421 ISSN 0096 3518 Matsuyama Y Gray R January 1981 Universal tree encoding for speech IEEE Transactions on Information Theory 27 1 31 40 doi 10 1109 tit 1981 1056306 ISSN 0018 9448 Matsuyama Y Gray R April 1982 Voice Coding and Tree Encoding Speech Compression Systems Based Upon Inverse Filter Matching IEEE Transactions on Communications 30 4 711 720 doi 10 1109 tcom 1982 1095512 ISSN 0090 6778 IEEE Fellows 1998 IEEE Communications Society External links editYasuo Matsuyama Professor Emeritus of Waseda University Yasuo Matsuyama at IEEE Retrieved from https en wikipedia org w index php title Yasuo Matsuyama amp oldid 1197375255, wikipedia, wiki, book, books, library,

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