Classification of time-series data using a generative/discriminative hybrid

Classification of Time-Series data using discriminative models such as SVMs is very hard due to the variable length of this type of data. On the other hand generative models such as HMMs have become the standard tool for modeling Time-Series data due to their efficiency. This paper proposes a general generative/discriminative hybrid that uses HMMs to map the variable length Time-Series data into a fixed P-dimensional vector that can be easily classified using any discriminative model. The hybrid system was tested on the MNIST database for unconstrained handwritten numerals and has achieved an improvement of 1.23% (on the test set) over traditional 2D discrete HMMs.