Today I read a paper titled “Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information”
The abstract is:
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics
In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW)
LSDTW finds an alignment that maximizes statistical dependency between sequences, measured by a squared-loss variant of mutual information
The benefit of this novel information-theoretic formulation is that LSDTW can align sequences with different lengths, different dimensionality, high non-linearity, and non-Gaussianity in a computationally efficient manner
In addition, model parameters such as an initial alignment matrix can be systematically optimized by cross-validation
We demonstrate the usefulness of LSDTW through experiments on synthetic and real-world Kinect action recognition datasets