13 citations · 52 across the 18 of their papers we have counts for
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stat.ML2013★ 10 cited
Clustering for high-dimension, low-sample size data using distance vectors
Yoshikazu Terada
In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is…
stat.ME2013★ 4 cited
Functional Factorial K-means Analysis
Michio Yamamoto, Yoshikazu Terada
A new procedure for simultaneously finding the optimal cluster structure of multivariate functional objects and finding the subspace to represent the cluster structure is presented…
math.ST2013★ 4 cited
Strong Consistency of Factorial K-means Clustering
Yoshikazu Terada
Factorial k-means (FKM) clustering is a method for clustering objects in a low-dimensional subspace. The advantage of this method is that the partition of objects and the low-dimen…