| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
Recently, hubness, a phenomenon occurring in high-dimensional datasets as a result of curse of dimensionality has attracted the attention of researchers in the artificial intelligence community, especially for data mining and machine learning.
In this work, we pointed out that the hubness influences the performance of k-nearest neighbor (k-NN) methods. We reported that subtracting mean vector from each sample (centering) is a simple, yet very effective for improving k-NN classification. Also, we proved that centering is effective for k-NNs, because centering reduces hubs in a dataset.
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