Several fuzzy clustering models were proposed by extending intrinsic fuzzy partition mechanisms of probabilistic mixture models and have been shown to have ability of improving the partition quality and interpretability of probabilistic partitions. In this paper, a novel fuzzy clustering interpretation of probabilistic latent semantic analysis (pLSA) is discussed and a fuzzy co-clustering model is proposed by introducing adjustable fuzzification penalty to the pseudo-log-likelihood function of pLSA. Several numerical experiments demonstrate the advantage of tuning the intrinsic fuzziness of pLSA likelihood function.
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