著者

Akisato Kimura Masashi Sugiyama Hitoshi Sakano Hirokazu Kameoka

雑誌

情報処理学会論文誌数理モデル化と応用(TOM) (ISSN:18827780)

巻号頁・発行日

vol.6, no.1, pp.136-145, 2013-03-12

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It is well known that dimensionality reduction based on multivariate analysis methods and their kernelized extensions can be formulated as generalized eigenvalue problems of scatter matrices, Gram matrices or their augmented matrices. This paper provides a generic and theoretical framework of multivariate analysis introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful. The framework includes not only (1) the traditional multivariate analysis methods but also (2) several regularization techniques, (3) localization techniques, (4) clustering methods based on generalized eigenvalue problems, and (5) their semi-supervised extensions. This paper also presents a methodology for designing a desired multivariate analysis method from the proposed framework. The methodology is quite simple: adopting the above mentioned special cases as templates, and generating a new method by combining these templates appropriately. Through this methodology, we can freely design various tailor-made methods for specific purposes or domains.

著者

Akisato KIMURA

出版者

国立情報学研究所

雑誌

Progress in informatics : PI (ISSN:13498614)

巻号頁・発行日

vol.11, pp.19-30, 2014-03

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ソーシャルネットワークサービス(SNS)で取り扱われるメディアは,従来から存在するマイクロブログ形式のテキストから,画像・映像等のマルチメディアコンテンツを含むものへ遷移し拡大してきている.これらSNS上のコンテンツは,ユーザ間の関係性や位置情報など,コンテンツの内容を知る上で非常に有用な補助情報が多数含まれている一方で,そのコンテンツがあまりにも膨大かつ多様であるため,自動的に解析することが容易ではない.本論文では,上記に示した有用性と問題点とのトレードオフを解決しうる1つの可能性として,ソーシャルキュレーションに着目する.ソーシャルキュレーションとは,SNS上のコンテンツを編集して新たなコンテンツを創る手動作業のことである.すなわち,このキュレーション後のコンテンツは,それ以前のコンテンツよりもはるかに洗練され,有用な情報が凝縮され,内容が絞り込まれている.このことは,コンテンツを解析する上でのコーパスとしての可能性を示すものである.上記の議論を踏まえ,本論文では,ソーシャルキュレーションに関する近年の動向,及びそのクロスメディア解析・マイニングへの利活用について概観する.

著者

Akisato KIMURA Kevin DUH Tsutomu HIRAO Katsuhiko ISHIGURO Tomoharu IWATA Albert AU YEUNG

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E97-D, no.6, pp.1557-1566, 2014-06-01

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Social media such as microblogs have become so pervasive such that it is now possible to use them as sensors for real-world events and memes. While much recent research has focused on developing automatic methods for filtering and summarizing these data streams, we explore a different trend called social curation. In contrast to automatic methods, social curation is characterized as a human-in-the-loop and sometimes crowd-sourced mechanism for exploiting social media as sensors. Although social curation web services like Togetter, Naver Matome and Storify are gaining popularity, little academic research has studied the phenomenon. In this paper, our goal is to investigate the phenomenon and potential of this new field of social curation. First, we perform an in-depth analysis of a large corpus of curated microblog data. We seek to understand why and how people participate in this laborious curation process. We then explore new ways in which information retrieval and machine learning technologies can be used to assist curators. In particular, we propose a novel method based on a learning-to-rank framework that increases the curator's productivity and breadth of perspective by suggesting which novel microblogs should be added to the curated content.

著者

Akisato Kimura Masashi Sugiyama Takuho Nakano Hirokazu Kameoka Hitoshi Sakano Eisaku Maeda Katsuhiko Ishiguro

雑誌

情報処理学会論文誌数理モデル化と応用(TOM) (ISSN:18827780)

巻号頁・発行日

vol.6, no.1, pp.128-135, 2013-03-12

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Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired samples for mitigating overfitting. Advantages of the proposed method over previously proposed methods are its computational efficiency and intuitive operationality: it smoothly bridges the generalized eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single eigenvalue problem as the original CCA.

著者

Akisato KIMURA Ryo YONETANI Takatsugu HIRAYAMA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E96-D, no.3, pp.562-578, 2013-03-01

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We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.