Discussion of “Influential features PCA for high dimensional clustering”
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《高维聚类的影响特征主成分分析》讨论
- 研究问题
- 这篇讨论文关注 IF-PCA 与已有特征选择及聚类方法的关系，并审视其高维高斯混合模型分析的理论设定。
- 方法与思路
- 讨论作者区分联合特征选择与聚类方法、先筛选后聚类的过滤方法，并评述稀疏均值、对角协方差和弱分离设定。
- 材料陈述的贡献
- 所给材料强调高维聚类中特征选择已有研究基础，建议比较 IF-PCA 与既有方法，并讨论主论文的模型及渐近框架。
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原文摘要
I commend Jin and Wang on a very interesting paper introducing a novel approach to feature selection within clustering and a detailed analysis of its clustering performance under a Gaussian mixture model.I shall divide my discussion into several parts: (i) prior work on feature selection and clustering; (ii) theoretical aspects; (iii) practical aspects; and finally (iv) some questions and directions for future research.On feature selection and clustering.Jin and Wang write that the idea of two-stage clustering, consisting of feature selection followed by clustering is not completely new, and cite a paper by Chan and Hall from 2010.I would like to point out that the fact that in high dimensional settings, feature selection is a critical component in successful clustering has been long recognized in the clustering community.Indeed, several methods that select variables on which to cluster have been proposed, see for example Friedman and Meulman (2004), Law, Figueiredo and Jain (2004), Witten and Tibshirani (2010) and references therein for earlier works.These methods are different from IF-PCA as they propose joint feature selection and clustering, solving the resulting non-convex problem by an expectation-minimization approach.However, similar in spirit to the approach presented here, there are also filter methods that choose variables irrespective of the clustering method that will follow, for example, Dash et al. (2002), He, Cai andNiyogi (2005).In this sense, and also from a practical perspective, it would be interesting to see a comparison of the proposed IF-PCA method to some of these methods and a discussion of their similarities and differences.Theoretical aspects.The model assumed by Jin and Wang is that of a Gaussian mixture model with diagonal covariance, where the mean vectors of the different classes are all sparse, so most features are distributed as a single Gaussian and only a few of them as a mixture of say K Gaussians with different means.The theoretical analysis considers the limit as both p, n → ∞, where even at the influential features, the separation between the unknown classes all tend to zero.