  Machine learning has been proven to be effective in various application
areas, such as object and speech recognition on mobile systems. Since a
critical key to machine learning success is the availability of large training
data, many datasets are being disclosed and published online. From a data
consumer or manager point of view, measuring data quality is an important first
step in the learning process. We need to determine which datasets to use,
update, and maintain. However, not many practical ways to measure data quality
are available today, especially when it comes to large-scale high-dimensional
data, such as images and videos. This paper proposes two data quality measures
that can compute class separability and in-class variability, the two important
aspects of data quality, for a given dataset. Classical data quality measures
tend to focus only on class separability; however, we suggest that in-class
variability is another important data quality factor. We provide efficient
algorithms to compute our quality measures based on random projections and
bootstrapping with statistical benefits on large-scale high-dimensional data.
In experiments, we show that our measures are compatible with classical
measures on small-scale data and can be computed much more efficiently on
large-scale high-dimensional datasets.
