iPCA: An Interactive System for PCA-based Visual Analytics
iPCA: An Interactive System for PCA-based Visual Analytics
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DOI：
10.1111/j.1467-8659.2009.01475.x
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发表时间：
2009-06-10
影响因子：
2.5
通讯作者：
Chang, Remco
中科院分区：
文献类型：
--
作者：
Jeong, Dong Hyun;Ziemkiewicz, Caroline;Chang, Remco
主成分分析(PCA)是一种广泛应用于因素和趋势分析、降维等领域的数学方法。然而，它通常被认为是一种黑箱操作，其结果很难解释，有时对用户来说是反直觉的。为了帮助用户更好地理解和利用主成分分析，我们开发了一个系统，使用多个协调视图和丰富的用户交互来可视化主成分分析的结果。我们的设计理念是通过与PCA输出的广泛交互来支持多变量数据集的分析。为了证明我们的系统的有效性，我们使用一个已知的商业系统SAS/Insight‘s Interactive Data Explore进行了一个比较的用户研究。在我们的研究中，参与者用每个界面解决了一些高级分析任务，并对系统的易学性和实用性进行了评级。基于参与者的准确性、速度和定性反馈，我们观察到我们的系统帮助用户更好地理解数据与计算的特征空间之间的关系，从而使参与者能够更准确地分析数据。用户反馈表明，我们系统的互动性和透明度是我们方法的关键优势。
Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However it is often considered to be a "black box" operation whose results are difficult to interpret and sometimes counter-intuitive to the user In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems oil ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.