Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series (Kadous – 2002)

Current Mood: studious

Blogs I Commented On:

• Pankaj Rajan
• Aaron Wolin
• Brandon Paulson

Summary:
The core idea behind this thesis work is taking advantage of multivariate time series in order to aid in hand gesture recognition accuracy. In particular, the author focuses on the sub-events that a human might detect as part of a sign within some sign language, or Australian Sign Language (Auslan) for this paper. The author goes on by saying that metafeatures can parameterize them by capturing their properties such as temporal characteristics within a parameter space, a 2-D space of time and height, for feature construction. The temporal classification system, which I’m guessing is called TClass, uses synthetic events found within this space for feature construction, and applying several metafeatures into training instances constructs synthetic features. This is done in order for the TClass to mix temporal and non-temporal features not found in other temporal classification systems, as claimed by the author. A motivation is to produce a temporal classifier which produces comprehensible yet accurate descriptions. The system was implemented on Auslan, a language where signs consists of a mixture of handshapes, location, orientation, movement, and expression. Data was collected on the Nintendo Powerglove and Flock. The first input device was very noisy and far inferior to the second device. Several machine learning techniques were tested in conjunction and in comparison with TClass. Some observations concerning the Flock data itself was that TClass didn’t perform well with the HMM, smoothing of data didn’t improve results, and that TClass can handle tons of data. Accuracy rates for the Flock data are stated to be at 98% accuracy on voting, which sounds like ensemble averaging.

Discussion:
For me, some of the results were a bit confusing for me to give a fair critique of what I thought of the performance the author’s system. This would warrant reading the rest of the thesis, but on areas which were clear, I thought it was a pretty good approach. The author also did a nice job in collection tons of data to build his system based on the more accurate Flock device, despite it not having multiple users like the Nintendo data. It seems like a sound approach with nice accuracy results, but comparisons to other temporal classifiers showing improvement would have made it better. I don’t think I saw them in the sections we were supposed to read.