A Common-Factor Approach for Multivariate Data Cleaning with an
  Application to Mars Phoenix Mission Data
  Data quality is fundamentally important to ensure the reliability of data for
stakeholders to make decisions. In real world applications, such as scientific
exploration of extreme environments, it is unrealistic to require raw data
collected to be perfect. As data miners, when it is infeasible to physically
know the why and the how in order to clean up the data, we propose to seek the
intrinsic structure of the signal to identify the common factors of
multivariate data. Using our new data driven learning method, the common-factor
data cleaning approach, we address an interdisciplinary challenge on
multivariate data cleaning when complex external impacts appear to interfere
with multiple data measurements. Existing data analyses typically process one
signal measurement at a time without considering the associations among all
signals. We analyze all signal measurements simultaneously to find the hidden
common factors that drive all measurements to vary together, but not as a
result of the true data measurements. We use common factors to reduce the
variations in the data without changing the base mean level of the data to
avoid altering the physical meaning.
