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Kazuo Hara

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4 papers

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AAAI Conference 2025 Conference Paper

Hubness Change Point Detection

Ikumi Suzuki
Kazuo Hara
Eiji Murakami

This study proposes a new change detection method that leverages hubness. Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. Hubness is known to degrade the accuracy of methods based on nearest neighbor search. Therefore, many studies in the past have focused on reducing hubness to improve accuracy. In contrast, this study utilizes hubness to detect changes. Specifically, if there is no change, suppressing the hubness occurring in the two datasets obtained by dividing the time series data will result in a uniform data distribution. However, if there is a change, even if we try to reduce the hubness in the two datasets obtained by dividing the time series data before and after the change, the hubness will not be reduced, and the data distribution will not become uniform. We use this finding to detect changes. Experiments with synthetic data show that the proposed method achieves accuracy comparable to or exceeding that of existing methods. Additionally, the proposed method achieves good accuracy with real-world data from hydraulic systems and gas sensors, along with excellent runtime performance.

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AAAI Conference 2016 Conference Paper

Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness

Kazuo Hara
Ikumi Suzuki
Kei Kobayashi
Kenji Fukumizu
Milos Radovanovic

Spatial centrality, whereby samples closer to the center of a dataset tend to be closer to all other samples, is regarded as one source of hubness. Hubness is well known to degrade k-nearest-neighbor (k-NN) classiﬁcation. Spatial centrality can be removed by centering, i. e. , shifting the origin to the global center of the dataset, in cases where inner product similarity is used. However, when Euclidean distance is used, centering has no effect on spatial centrality because the distance between the samples is the same before and after centering. As described in this paper, we propose a solution for the hubness problem when Euclidean distance is considered. We provide a theoretical explanation to demonstrate how the solution eliminates spatial centrality and reduces hubness. We then present some discussion of the reason the proposed solution works, from a viewpoint of density gradient, which is regarded as the origin of spatial centrality and hubness. We demonstrate that the solution corresponds to ﬂattening the density gradient. Using real-world datasets, we demonstrate that the proposed method improves k-NN classiﬁcation performance and outperforms an existing hub-reduction method.

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AAAI Conference 2015 Conference Paper

Localized Centering: Reducing Hubness in Large-Sample Data

Kazuo Hara
Ikumi Suzuki
Masashi Shimbo
Kei Kobayashi
Kenji Fukumizu
Miloš Radovanović

Hubness has been recently identified as a problematic phenomenon occurring in high-dimensional space. In this paper, we address a different type of hubness that occurs when the number of samples is large. We investigate the difference between the hubness in highdimensional data and the one in large-sample data. One finding is that centering, which is known to reduce the former, does not work for the latter. We then propose a new hub-reduction method, called localized centering. It is an extension of centering, yet works effectively for both types of hubness. Using real-world datasets consisting of a large number of documents, we demonstrate that the proposed method improves the accuracy of knearest neighbor classification.

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AAAI Conference 2012 Conference Paper

Investigating the Effectiveness of Laplacian-Based Kernels in Hub Reduction

Ikumi Suzuki
Kazuo Hara
Masashi Shimbo
Yuji Matsumoto
Marco Saerens

A “hub” is an object closely surrounded by, or very similar to, many other objects in the dataset. Recent studies by Radovanović et al. indicate that in high dimensional spaces, hubs almost always emerge, and objects close to the data centroid tend to become hubs. In this paper, we show that the family of kernels based on the graph Laplacian makes all objects in the dataset equally similar to the centroid, and thus they are expected to make less hubs when used as a similarity measure. We investigate this hypothesis using both synthetic and real-world data. It turns out that these kernels suppress hubs in some cases but not always, and the results seem to be affected by the size of the data. However, for the datasets in which hubs are indeed reduced by the Laplacian-based kernels, these kernels work well in ranking and classification tasks. This result suggests that the amount of hubs, which can be readily computed in an unsupervised fashion, can be a yardstick of whether Laplacian-based kernels work effectively for a given data.

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