439 research outputs found
Characteristics of networks generated by kernel growing neural gas
This research aims to develop kernel GNG, a kernelized version of the growing
neural gas (GNG) algorithm, and to investigate the features of the networks
generated by the kernel GNG. The GNG is an unsupervised artificial neural
network that can transform a dataset into an undirected graph, thereby
extracting the features of the dataset as a graph. The GNG is widely used in
vector quantization, clustering, and 3D graphics. Kernel methods are often used
to map a dataset to feature space, with support vector machines being the most
prominent application. This paper introduces the kernel GNG approach and
explores the characteristics of the networks generated by kernel GNG. Five
kernels, including Gaussian, Laplacian, Cauchy, inverse multiquadric, and log
kernels, are used in this study
An efficient and straightforward online quantization method for a data stream through remove-birth updating
The growth of network-connected devices is creating an explosion of data,
known as big data, and posing significant challenges to efficient data
analysis. This data is generated continuously, creating a dynamic flow known as
a data stream. The characteristics of a data stream may change dynamically, and
this change is known as concept drift. Consequently, a method for handling data
streams must efficiently reduce their volume while dynamically adapting to
these changing characteristics. This paper proposes a simple online vector
quantization method for concept drift. The proposed method identifies and
replaces units with low win probability through remove-birth updating, thus
achieving a rapid adaptation to concept drift. Furthermore, the results of this
study show that the proposed method can generate minimal dead units even in the
presence of concept drift. This study also suggests that some metrics
calculated from the proposed method will be helpful for drift detection
A Clustering Method for Data in Cylindrical Coordinates
We propose a new clustering method for data in cylindrical coordinates based on the k-means. The goal of the k-means family is to maximize an optimization function, which requires a similarity. Thus, we need a new similarity to obtain the new clustering method for data in cylindrical coordinates. In this study, we first derive a new similarity for the new clustering method by assuming a particular probabilistic model. A data point in cylindrical coordinates has radius, azimuth, and height. We assume that the azimuth is sampled from a von Mises distribution and the radius and the height are independently generated from isotropic Gaussian distributions. We derive the new similarity from the log likelihood of the assumed probability distribution. Our experiments demonstrate that the proposed method using the new similarity can appropriately partition synthetic data defined in cylindrical coordinates. Furthermore, we apply the proposed method to color image quantization and show that the methods successfully quantize a color image with respect to the hue element
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