iPCA: An Interactive System for PCA‐based Visual Analytics

TLDR

Principal Component Analysis is widely used for dimensionality reduction and trend analysis, yet it is often viewed as a black‑box technique whose outputs are difficult to interpret. The authors created iPCA, an interactive system that visualizes PCA results through coordinated views and rich user interactions to aid understanding and utilization. iPCA employs multiple coordinated visualizations and interaction techniques, and its effectiveness was evaluated in a comparative user study against SAS/INSIGHT’s Interactive Data Exploration. The study showed that iPCA improves users’ comprehension of data–eigenspace relationships, leading to more accurate analysis, with interactivity and transparency identified as its main strengths.

Abstract

Abstract 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 on 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.