SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.     

哈佛服务利润链理论及其隐义引申

《哈佛商业评论》1994年首次发表、2008年原文重刊的"让服务利润链有效作用"一文,"展示了一个简洁而无比明了的增加服务企业利润的方法"。要使服务利润链"链条"能够正相关联动起来,不仅外部服务要为顾客创造出高的顾客让渡价值,更重要的是通过高质量的内部服务为一线员工创造高的"内部顾客让渡价值"。在介绍服务利润链(SPC)理论形成溯源、走向成熟及其理论精髓的基础上,笔者引申了SPC隐义,认为:SPC强调成本收益的比较,每个链环都有产生"利润"的使命;SPC存在关键链环,不同业态特征关键链环不尽相同,不同发展时期关键链环会有所转化;SPC要求建立内部服务"质量拳"概念,从五个方面构筑优质内部服务质量;SPC关注许多难以计量却又对服务业赢利至关重要的指标参数,服务利润链审计可以借鉴平衡计分卡(BSC)方法发展切合自身特点的SPCA工具。     

重点学术期刊专项基金管理中的期刊评价——基于简化的区间数据主成分分析方法

国家自然科学基金委设立"重点学术期刊专项基金"资助国内优秀学术期刊,在优秀期刊的遴选和资助效果评价过程中,存在期刊数量大,评价指标多等问题.基于此,提出一种针对大规模高维数据的简化的区间数据主成分分析方法(simplified principal component analysis, SPCA).该方法将区间主成分分析分解成2个基本阶段:第一是如何更加简单和高精度地计算数据集合的主轴,第二是如何绘制可视性与可解释性都更强的主平面图,以增强研究人员对大规模数据主要特征的洞察能力.采用SPCA方法,一方面能够从整体上研究各学科期刊的差异,说明学科之间的不可比较性;另一方面能够筛选出衡量期刊水平的关键指标,遴选优秀期刊,同时分析连续受资助期刊的动态资助效果.     

Least Squares Sparse Principal Component Analysis: A Backward Elimination Approach to Attain Large Loadings

Sparse principal components analysis (SPCA) is a technique for finding principal components with a small number of non‐zero loadings. Our contribution to this methodology is twofold. First we derive the sparse solutions that minimise the least squares criterion subject to sparsity requirements. Second, recognising that sparsity is not the only requirement for achieving simplicity, we suggest a backward elimination algorithm that computes sparse solutions with large loadings. This algorithm can be run without specifying the number of non‐zero loadings in advance. It is also possible to impose the requirement that a minimum amount of variance be explained by the components. We give thorough comparisons with existing SPCA methods and present several examples using real datasets.