Y
Yui Noma
Researcher at Fujitsu
Publications - 21
Citations - 47
Yui Noma is an academic researcher from Fujitsu. The author has contributed to research in topics: Feature vector & Hyperplane. The author has an hindex of 4, co-authored 21 publications receiving 47 citations.
Papers
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Hyperplane Arrangements and Locality-Sensitive Hashing with Lift
Makiko Konoshima,Yui Noma +1 more
TL;DR: A lift map that converts learning algorithms without the offsets to the ones that take into account the offsets is proposed and studied the relationship between the statistical characteristics of data, the number of hyperplanes, and the effect of the proposed method.
Patent
Information conversion method, information conversion device, and recording medium
Makiko Konoshima,Yui Noma +1 more
TL;DR: In this paper, the authors define a probability density function to indicate the probability of existence of a particle on a unit sphere, and convert the feature vector to a binary string, considering a positional vector of the moved particle as a normal vector of a hyperplane, by the processor.
Posted Content
Locality-Sensitive Hashing with Margin Based Feature Selection
Makiko Konoshima,Yui Noma +1 more
TL;DR: This method can effectively perform optimization for cases such as fingerprint images with a large number of labels and extremely few data that share the same labels, as well as verifying that it is also effective for natural images, handwritten digits, and speech features.
Journal ArticleDOI
Markov Chain Monte Carlo for Arrangement of Hyperplanes in Locality-Sensitive Hashing
Yui Noma,Makiko Konoshima +1 more
TL;DR: A supervised learning method for hyperplane arrangements in feature space that uses a Markov chain Monte Carlo (MCMC) method and its accuracy when using a suitable probability density function and sampling method is greater than the accuracy of existing learning methods.
Locality-Sensitive Hashing with Margin Based Feature Selection
Makiko Konoshima,Yui Noma +1 more
TL;DR: In this article, a learning method with feature selection for Locality-Sensitive Hashing is proposed, which uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used.