Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly

Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly

Blog Article

When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls.A principal curve Door Seal End acts as a nonlinear generalization of PCA, and the present paper proposes a novel algorithm to automatically and sequentially learn principal curves from data streams.We show that our procedure is supported by regret bounds with optimal sublinear remainder terms.

A greedy local search implementation (called slpc, for sequential learning principal curves) that incorporates both sleeping experts and multi-armed bandit ingredients is presented, along with Adrenal Support its regret computation and performance on synthetic and real-life data.