Algorithms Challenges

Algorithms for subspace learning can be categorized following their properties:

• Provably correct and under what assumptions?
• Practical accuracy.
• Time complexity.
• Memory complexity and/or number of passes.
• Incremental versus batch.
• Online versus offline.
• Sequential versus parallel.
• Streaming and nearly-streaming
• Real-time or not.

Please see for details the following paper:

N. Vaswani, T. Bouwmans, S. Javed, P. Narayanamurthy, “Robust Principal Component Analysis, Subspace Learning, and Tracking”, IEEE Signal Processing Letters, July 2018. [pdf]