The LCSS algorithm (from which WarpingLCSS is an optimization) operates on a sequence of discrete symbols such as letters or nucleotides. This is in contrast to a neutral network whose natural inputs come from a continuous vector space. IMU data is continuous. We needed to bucket it before feeding it into LCSS. The random forest buckets continuous data into discrete categories. We tried support vector machines too but random forest classification worked better.
Aha—so like how something like word2vec maps discrete symbols into a shared vector space, this does the other direction, mapping vectors into discrete symbols. Never thought about that direction; thanks!
“Turn the continuous datastram into discrete symbols by feeding it into a random forest classifier.”
Would you be willing to say any more about this? It sounds really interesting.
The LCSS algorithm (from which WarpingLCSS is an optimization) operates on a sequence of discrete symbols such as letters or nucleotides. This is in contrast to a neutral network whose natural inputs come from a continuous vector space. IMU data is continuous. We needed to bucket it before feeding it into LCSS. The random forest buckets continuous data into discrete categories. We tried support vector machines too but random forest classification worked better.
Aha—so like how something like word2vec maps discrete symbols into a shared vector space, this does the other direction, mapping vectors into discrete symbols. Never thought about that direction; thanks!