{"id":1485,"date":"2017-10-20T10:50:17","date_gmt":"2017-10-20T01:50:17","guid":{"rendered":"http:\/\/www.cns.atr.jp\/dbi\/?page_id=1485\/"},"modified":"2017-10-20T11:26:35","modified_gmt":"2017-10-20T02:26:35","slug":"machinelearninglifelogjp","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/dbi\/introduction-ja\/machinelearninglifelogjp\/","title":{"rendered":"\u8133\u60c5\u5831\u30fb\u65e5\u5e38\u751f\u6d3b\u884c\u52d5\u89e3\u6790"},"content":{"rendered":"

\u6700\u8fd1\u306e\u4eba\u5de5\u77e5\u80fd\u306e\u76ee\u899a\u307e\u3057\u3044\u767a\u5c55\u306b\u3082\u304b\u304b\u308f\u3089\u305a\u3001\u4f8b\u3048\u3070\u5bb6\u4e2d\u3092\u304d\u308c\u3044\u306b\u7247\u4ed8\u3051\u3066\u304f\u308c\u308b\u30ed\u30dc\u30c3\u30c8\u306f\u672a\u3060\u5b9f\u73fe\u3057\u3066\u3044\u307e\u305b\u3093\u3002\u9593\u53d6\u308a\u3001\u5bb6\u5177\u3001\u6563\u3089\u304b\u308a\u5177\u5408\u306a\u3069\u5343\u5dee\u4e07\u5225\u306a\u5bb6\u306e\u72b6\u6cc1\u306b\u5bfe\u5fdc\u3059\u308b\u306b\u306f\u3001\u6975\u3081\u3066\u9ad8\u5ea6\u306a\u6c4e\u5316\u80fd\u529b\u304c\u5fc5\u8981\u3068\u306a\u308b\u304b\u3089\u3067\u3059\u3002\u79c1\u305f\u3061\u306f\u3001\u8133\u6d3b\u52d5\u30c7\u30fc\u30bf\u3084\u30e9\u30a4\u30d5\u30ed\u30b0\u3092\u7d71\u5408\u7684\u306b\u89e3\u6790\u3059\u308b\u624b\u6cd5\u3092\u958b\u767a\u3059\u308b\u3053\u3068\u3067\u3001\u8907\u96d1\u306a\u65e5\u5e38\u74b0\u5883\u3092\u628a\u63e1\u3057\u3001\u9069\u5207\u306a\u884c\u52d5\u304c\u3068\u308c\u308b\u30d2\u30c8\u306e\u80fd\u529b\u3092\u30c7\u30fc\u30bf\u99c6\u52d5\u7684\u306b\u63a2\u7a76\u3059\u308b\u3068\u3068\u3082\u306b\u3001\u5f97\u3089\u308c\u305f\u77e5\u898b\u3092\u6d3b\u304b\u3057\u305f\u77e5\u7684\u30b7\u30b9\u30c6\u30e0\u306e\u69cb\u7bc9\u3084\u3001\u65e5\u5e38\u751f\u6d3b\u652f\u63f4\u30b5\u30fc\u30d3\u30b9\u306e\u5275\u51fa\u3092\u76ee\u6307\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n

\u7814\u7a76\u30c6\u30fc\u30de
\n1. \u30de\u30eb\u30c1\u30bb\u30f3\u30b5\u30c7\u30fc\u30bf\u306e\u89e3\u6790\u6cd5
\n\u30d2\u30c8\u306f\u8907\u6570\u306e\u611f\u899a\u60c5\u5831\u3092\u7d71\u5408\u3057\u3066\u5468\u56f2\u306e\u72b6\u6cc1\u3092\u7406\u89e3\u3057\u3001\u884c\u52d5\u3057\u3066\u3044\u307e\u3059\u3002\u77e5\u7684\u30b7\u30b9\u30c6\u30e0\u306e\u65e5\u5e38\u74b0\u5883\u5185\u3067\u306e\u72b6\u6cc1\u628a\u63e1\u80fd\u529b\u3092\u9ad8\u3081\u308b\u305f\u3081\u3001\u30d2\u30c8\u306e\u65e5\u5e38\u751f\u6d3b\u884c\u52d5\u4e2d\u306e\u69d8\u3005\u306a\u30bb\u30f3\u30b5\u30c7\u30fc\u30bf\u3092\u7d71\u5408\u7684\u306b\u89e3\u6790\u3059\u308b\u624b\u6cd5\u306e\u7814\u7a76\u958b\u767a\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002[1]\u3067\u306f\u3001\u4e00\u4eba\u79f0\u8996\u70b9\u6620\u50cf\u3068\u624b\u30fb\u982d\u306e\u52d5\u304d\uff08\u52a0\u901f\u5ea6\uff09\u306e\u95a2\u9023\u3092\u3068\u3089\u3048\u308b\u7279\u5fb4\u91cf\u8868\u73fe\u3092\u62bd\u51fa\u3057\u3001\u3053\u308c\u3092\u5229\u7528\u3057\u3066\u3001\u30b8\u30a7\u30b9\u30c1\u30e3\u304b\u3089\u904e\u53bb\u306b\u540c\u69d8\u306e\u52d5\u4f5c\u3092\u3057\u305f\u3068\u304d\u306b\u898b\u3066\u3044\u305f\u6620\u50cf\u3092\u691c\u7d22\u3059\u308b\u8a18\u61b6\u88dc\u52a9\u30b7\u30b9\u30c6\u30e0\u3092\u69cb\u7bc9\u3057\u307e\u3057\u305f\u3002\u307e\u305f\u3001[2]\u3067\u306f\u3001\u4e00\u4eba\u79f0\u8996\u70b9\u6620\u50cf\u3068\u52a0\u901f\u5ea6\u3092\u540c\u6642\u8a08\u6e2c\u3057\u305f\u30b9\u30c8\u30ea\u30fc\u30e0\u30c7\u30fc\u30bf\u306e\u4e2d\u304b\u3089\u3001\u65e5\u5e38\u884c\u52d5\u4e2d\u306a\u3069\u91cd\u8981\u306a\u60c5\u5831\u3092\u6301\u3063\u3066\u3044\u308b\u6642\u533a\u9593\u3092\u62bd\u51fa\u3059\u308b\u305f\u3081\u306e\u6a5f\u68b0\u5b66\u7fd2\u6cd5\u3092\u958b\u767a\u3057\u307e\u3057\u305f\u3002<\/p>\n

[1] Miyanishi et al., \u201cEgocentric video search via physical interaction\u201d, Proc. of the 30th AAAI Conf. on Artificial Intelligence (AAAI-16), Vol.1, pp.330-336, 2016
\n[2] Li et al., \u201cKey frame extraction from first-person video with multi-sensor integration\u201d, Proc. of the IEEE Int. Conf. on Multimedia and Expo (ICME), 2017.<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

2. \u8133\u6d3b\u52d5\u306e\u500b\u4eba\u5dee\u30fb\u6642\u9593\u5909\u52d5\u306e\u89e3\u6790\u6cd5
\n\u65e5\u5e38\u751f\u6d3b\u884c\u52d5\u306f\u305d\u306e\u4eba\u306e\u500b\u6027\u3084\u305d\u306e\u6642\u306e\u72b6\u6cc1\u306b\u5927\u304d\u304f\u5f71\u97ff\u3055\u308c\u307e\u3059\u3002\u65e5\u5e38\u751f\u6d3b\u652f\u63f4\u3092\u500b\u4eba\u306b\u3088\u308a\u9069\u3057\u305f\u3082\u306e\u306b\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u3053\u306e\u3088\u3046\u306a\u60c5\u5831\u3092\u77e5\u7684\u30b7\u30b9\u30c6\u30e0\u306b\u53d6\u308a\u8fbc\u3080\u3053\u3068\u304c\u5fc5\u8981\u3067\u3059\u3002\u8133\u5185\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u7d50\u5408\u306e\u500b\u4eba\u5dee\u3084\u6642\u9593\u5909\u52d5\u3092\u8133\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u30c7\u30fc\u30bf\u304b\u3089\u7406\u89e3\u3057\u3084\u3059\u3044\u5f62\u3067\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u3092\u958b\u767a\u3057\u307e\u3057\u305f[3]\u3002\u3053\u308c\u3092\u3055\u3089\u306b\u9ad8\u5ea6\u5316\u3057\u3066\u3001\u8133\u5185\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u69cb\u9020\u3068\u30e2\u30b8\u30e5\u30fc\u30eb\u9593\u7d50\u5408\u306e\u5909\u52d5\u3092\u540c\u6642\u63a8\u5b9a\u3059\u308b\u65b9\u6cd5\u3092\u958b\u767a\u3057\u307e\u3057\u305f[4]\u3002\u5927\u898f\u6a21\u5b89\u9759\u6642fMRI\u30c7\u30fc\u30bf\u306b\u9069\u7528\u3057\u305f\u3068\u3053\u308d\u3001\u2460\u5b89\u9759\u6642\u306b\u6d3b\u52d5\u3059\u308bDMN\u3001\u2461\u611f\u899a\u7cfb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u2462Saliency\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u62bd\u51fa\u3055\u308c\u3001\u2462\u304c\u2460\u3068\u2461\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5207\u308a\u66ff\u3048\u3092\u884c\u3046\u3068\u3044\u3046\u4eee\u8aac\u3092\u793a\u5506\u3059\u308b\u30e2\u30b8\u30e5\u30fc\u30eb\u9593\u7d50\u5408\u306e\u5909\u52d5\u30d1\u30bf\u30fc\u30f3\u304c\u5f97\u3089\u308c\u307e\u3057\u305f\u3002<\/p>\n

[3] Hyvaerinen et al., \u201cOrthogonal connectivity factorization: Interpretable decomposition of variability in correlation matrices\u201d, Neural Computation, Vol.28, No.3, pp.445-484, 2016.
\n[4] Hirayama et al., \u201cCharacterizing variability of modular brain connectivity with constrained principal component analysis\u201d, PLoS ONE, Vol.11, No.12, e0168180.<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

3. \u79fb\u52d5\u30ed\u30dc\u30c3\u30c8\u306b\u3088\u308b\u65e5\u5e38\u74b0\u5883\u30bb\u30f3\u30b7\u30f3\u30b0
\n\u30d7\u30e9\u30a4\u30d9\u30fc\u30c8\u306a\u74b0\u5883\u306e\u30bb\u30f3\u30b7\u30f3\u30b0\u306b\u304a\u3044\u3066\u306f\u3001\u591a\u6570\u306e\u76e3\u8996\u30ab\u30e1\u30e9\u306f\u30d2\u30c8\u306b\u4e0d\u5b89\u611f\u3092\u4e0e\u3048\u3066\u3057\u307e\u3046\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u79c1\u305f\u3061\u306f\u3001\u305d\u306e\u4ee3\u66ff\u6848\u3068\u3057\u3066\u79fb\u52d5\u30ed\u30dc\u30c3\u30c8\u306b\u3088\u308b\u65e5\u5e38\u74b0\u5883\u306e\u60c5\u5831\u53ce\u96c6\u30b7\u30b9\u30c6\u30e0\u306e\u7814\u7a76\u958b\u767a\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002\u65e5\u5e38\u74b0\u5883\u3084\u305d\u3053\u306b\u3044\u308b\u4eba\u306e\u72b6\u614b\u3092\u7406\u89e3\u3057\u306a\u304c\u3089\u3001\u306a\u308b\u3079\u304f\u4eba\u306e\u90aa\u9b54\u3092\u3059\u308b\u3053\u3068\u306a\u304f\u3001\u52b9\u7387\u7684\u306b\u60c5\u5831\u53ce\u96c6\u3067\u304d\u308b\u79fb\u52d5\u30ed\u30dc\u30c3\u30c8\u3092\u76ee\u6307\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

\u30e1\u30f3\u30d0\u30fc<\/p>\n

\u30fb\u5ddd\u934b \u4e00\u6643
\n\u30fb\u5bae\u897f\u5927\u6a39
\n\u30fbMarc Patrick Zapf
\n\u30fb\u5e73\u5c71\u6df3\u4e00\u90ce\uff08\u7406\u7814AIP\uff0fATR\uff09
\n\u30fb\u517c\u6751\u539a\u7bc4\uff08\u7523\u7dcf\u7814\uff0fATR\uff09
\n\u30fbLuis Yoichi Morles\uff08\u540d\u53e4\u5c4b\u5927\u5b66\uff0fATR\uff09
\n\u30fbAapo Hyv\u00e4rinen (UCL\/U-Helsinki)
\n\u30fbHiroshi Morioka (UCL\/ATR)
\n\u30fbHande Celikkanat (U. Helsinki)<\/p>\n

\u30d7\u30ed\u30b8\u30a7\u30af\u30c8
\n– CREST\u77e5\u7684\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u9818\u57df \u300c\u795e\u7d4c\u79d1\u5b66\u306e\u516c\u7406\u7684\u8a08\u7b97\u8ad6\u3068\u9ad8\u984d\u306e\u69cb\u6210\u8ad6\u306e\u878d\u5408\u306b\u3088\u308b\u4eba\u5de5\u610f\u8b58\u306e\u69cb\u7bc9\u3068\u305d\u306e\u5b9f\u751f\u6d3b\u7a7a\u9593\u3078\u306e\u5b9f\u88c5\u300d\uff08\u4ee3\u8868\uff1a\u91d1\u4e95\u826f\u592a\uff09
\n– JST SICORP \u65e5\u672c-\u30d5\u30a3\u30f3\u30e9\u30f3\u30c9\u5171\u540c\u7814\u7a76 \u300c\u611f\u60c5\u30fb\u6c17\u5206\u60c5\u5831\u306e\u6b21\u4e16\u4ee3\u30e9\u30a4\u30d5\u30ed\u30b0\uff1a\u591a\u7a2e\u30bb\u30f3\u30b5\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u6a5f\u68b0\u5b66\u7fd2\u300d\uff08\u4ee3\u8868\uff1aA. Hyv\u00e4rinen, \u5ddd\u934b\u4e00\u6643\uff09
\n– \u79d1\u7814\u8cbb \u57fa\u76e4B \u300c\u591a\u5229\u7528\u8005\u30fb\u591a\u72b6\u6cc1\u306b\u5171\u901a\u3059\u308b\u7279\u6027\u306e\u62bd\u51fa\u306b\u3088\u308b\u60c5\u5831\u8ee2\u79fbBMI\u300d\uff08\u4ee3\u8868\uff1a\u5ddd\u934b\u4e00\u6643\uff09
\n– RIKEN AIP \u8133\u60c5\u5831\u7d71\u5408\u89e3\u6790\u30c1\u30fc\u30e0\uff08TL\uff1a\u5ddd\u934b\u4e00\u6643\uff09<\/p>\n","protected":false},"excerpt":{"rendered":"

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