| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2020: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2019: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2018: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
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| Outline of Final Research Achievements |
The overall aim of this computer science project was to tackle the problem of the curse of dimensionality in similarity applications for complex data types such as feature ensembles, graphs and networks, through the further development of practical techniques that recognize and take advantage of local variations in the intrinsic dimensionality of the data. The main goals were: (1) to advance the existing theory of intrinsic dimensionality to account for combinatorial data types; (2) to confirm the theoretical implications by means of empirical study; (3) to exploit new advances in the theory of local intrinsic dimensionality (LID) to develop more efficient and effective solutions for applications of databases, data mining and multimedia, particularly for graphs, deep neural networks, feature ensembles, and other complex data types.
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