Projected support points: a new method for high-dimensional data reduction

Understand

In an era where big and high-dimensional data is readily available, data scientists are inevitably faced with the challenge of reducing this data for expensive downstream computation or analysis.

• To this end, we present here a new method for reducing high-dimensional big data into a representative point set, called projected support points (PSPs).
• A key ingredient in our method is the so-called sparsity-inducing (SpIn) kernel, which encourages the preservation of low-dimensional features when reducing high-dimensional data.
• We begin by introducing a unifying theoretical framework for data reduction, connecting PSPs with fundamental sampling principles from experimental design and Quasi-Monte Carlo.