@inproceedings{ea99a91783a84408be8262060162f07c,

title = "Patchworking multiple pairwise distances for learning with distance matrices",

abstract = "A classification framework using only a set of distance matrices is proposed. The proposed algorithm can learn a classifier only from a set of distance matrices or similarity matrices, hence applicable to structured data, which do not have natural vector representation such as time series and graphs. Random forest is used to explore ideal feature representation based on the distance between points defined by a set of given distance matrices. The effectiveness of the proposed method is evaluated through experiments with point process data and graph structured data.",

keywords = "Classification, Decision trees, Graph kernel, Random forest, Spike train, Structured data",

author = "Ken Takano and Hideitsu Hino and Yuki Yoshikawa and Noboru Murata",

year = "2015",

doi = "10.1007/978-3-319-22482-4_33",

language = "English",

isbn = "9783319224817",

volume = "9237",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "287--294",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

}