4. SIGKDD: Intʼ’l Conf. on Knowledge Discovery
and Data Mining
l ACM データマイニング/機械学習の最難関国際会議
l 理理論論的な保証と同時に実験での(特に⼤大規模)評価が必要
l 今年年は8⽉月中旬にシカゴで開催
l Best Research Paper Award: Edo Liberty (Yahoo! Labs, Haifa)
“Simple and Deterministic Matrix Sketching”
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27. そこんとこEdo Liberty本⼈人に聞いてみました
l This indeed can be used but I thought it will be less efficient
in practice and more complicated to code. So, I did not
include it in the paper.
l Theoretically though, it can reduce the space usage by a
factor of 2, which theoretical CS people think is not
important :)
l That said, I received quite a few questions about that so I will
say something about it in the journal version.
l incremental rank-‐‑‒1 SVD updatesも同じように使えると思うよ
l けど実⽤用的には効率率率悪いし実装するのも難しいよね
l だからSIGKDDの論論⽂文には⼊入れなかったよ
l けど少なくともメモリ使⽤用量量は桁違いに良良いはずだよ
l そこは理理論論の⼈人は気にしないのかもしれないけど…
l まぁ同じ質問受けまくるからジャーナル版では何か書くよ
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