A Spatio-temporal Extension to Isomap Nonlinear Dimension Reduction (Jenkins & Matari – 2004)

Current Mood: studious

Blogs I Commented On:

• Josh Johnston
• Pankaj Rajan
• Brandon Paulson

Summary:
The focus of this paper appears to be in efficiently uncovering the structure of motion using unsupervised learning for dimension reduction. The authors use a spatio-temporal Isomap approach for both continuous and segmented input data with sequential temporal ordering, where continous ST-Isomap is suited for uncovering spatio-temporal manifolds of data, and segmented ST-Isomap is for uncovering spatio-temporal clusters in segmented data. Their technique tries to address temporal relationships of proximal disambiguation and distal correspondence in order to uncover the spatio-temporal structure. Their example of the two relationships is two low waving motions of different directions, and also a low and high motion of the same direction. In the former, the two motions fall in proximal disambiguation, and in the latter, the two motions fall in distal correspondence. Their ST-Isomap approach extends an Isomap approach by having temporal windowing to provide a temporal history for each data point, hard spatio-temporal correspondences between proximal data pairs, and distance between data airs with spatio temporal relationships to accentuate their similarity.

Discussion:
I honestly had no idea what this paper was talking about most of the time. Most of it was the lingering feeling that I couldn’t find an aspect of this paper that would be relevant to the topics we are doing in the class. But I think it’s safe to say that this a nice paper to refer to if one wishes to use unsupervised learning in hand motion.