Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Atsutoshi Kumagai"'
Autor:
Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda
Publikováno v:
Data Science and Engineering, Vol 8, Iss 3, Pp 279-291 (2023)
Abstract Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matri
Externí odkaz:
https://doaj.org/article/9f96328299044e299ef2c1e3deef884f
Publikováno v:
Data Science and Engineering, Vol 5, Iss 2, Pp 140-151 (2020)
Abstract We propose a transfer metric learning method to infer domain-specific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains. When training and test distrib
Externí odkaz:
https://doaj.org/article/1189426fc9c24db98a792387b3a9bdf3
Autor:
Atsutoshi Kumagai
Publikováno v:
NTT Technical Review. 19:12-15
Autor:
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Yuuki Yamanaka, Hisashi Kashima
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Publikováno v:
2022 International Joint Conference on Neural Networks (IJCNN).
Publikováno v:
Proceedings of the VLDB Endowment. 14:916-928
Anchor graph hashing is used in many applications such as cancer detection, web page classification, and drug discovery. It computes the hash codes from the eigenvectors of the matrix representing the similarities between data points and anchor point
Publikováno v:
Data Science and Engineering, Vol 5, Iss 2, Pp 140-151 (2020)
ICDM
ICDM
We propose a transfer metric learning method to infer domain-specific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains. When training and test distributions ar
Publikováno v:
CIKM
Anchor graphs are a popular tool used in label prediction of sparsely labeled data. In anchor graphs, labels of labeled data are propagated to unlabeled data via anchor points; anchor points are the centers of k-means clusters. Anchor graph-based lab
Publikováno v:
IJCNN
We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anomaly detection methods it is usually assumed that instances are independ
Publikováno v:
ICDE
Data visualization has become a fundamental process of data engineering. t-SNE is one of the most popular data visualization approaches. However, its computation cost is quadratic to the number of data points because it needs to compute similarities