生理時系列データの個人化畳み込み辞書学習
Personalized Convolutional Dictionary Learning of Physiological Time Series
集団共通のグローバル辞書と個人差を表す局所変換を組み合わせたPersonalized CDLを提案し、歩行データなどで有効性を示した。
著者: Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus, Laurent Oudre
分類: stat.ML, cs.LG, math.ST
原文アブストラクト
Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For instance, kinetic measurements of the gait cycle during locomotion present common characteristics, although idiosyncrasies may be observed due to biomechanical disposition or pathology. To better represent datasets with local-global structure, this work extends Convolutional Dictionary Learning (CDL), a popular method for learning interpretable representations, or dictionaries, of time-series data. In particular, we propose Personalized CDL (PerCDL), in which a local dictionary models local information as a personalized spatiotemporal transformation of a global dictionary. The transformation is learnable and can combine operations such as time warping and rotation. Formal computational and statistical guarantees for PerCDL are provided and its effectiveness on synthetic and real human locomotion data is demonstrated.