Page 285 - Contributed Paper Session (CPS) - Volume 7

CPS2099 Takatsugu Yoshioka et al.
4. Discussion and Conclusion
We propose RKM with NLPCA which combines NLPCA with k-means
clustering to observe the clusters of objects in data including categorical
variables in a low-dimensional subspace. The proposed method can basically
provide the estimations of component scores, clusters with their centroids,
and loadings in the subspace and reproduce the low-dimensional structure
well.
We have to investigate the performance in detail, for examples, how the
proposed method behaves for more complex date and how to avoid a local
minima problem, and compare the proposed method with other methods in
previous studies.

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