Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence Modeling

Abstract

Conditional Random Fields (CRF) are frequently applied for labeling and segmenting sequence data. introduced hidden state variables in a labeled CRF structure in order to model the latent dynamics within class labels, thus improving the labeling performance. Such a model is known as Latent-Dynamic CRF (LDCRF). We present Factored LDCRF (FLDCRF), a structure that allows multiple latent dynamics of the class labels to interact with each other. Including such latent-dynamic interactions leads to improved labeling performance on single-label and multi-label sequence modeling tasks. We apply our FLDCRF models on two single-label (one nested cross-validation) and one multi-label sequence tagging (nested cross-validation) experiments across two different datasets -UCI gesture phase data and UCI opportunity data. FLDCRF outperforms all state-of-the-art sequence models, i.e., CRF, LDCRF, LSTM, LSTM-CRF, Factorial CRF, Coupled CRF and a multi-label LSTM model in all our experiments. In addition, LSTM based models display inconsistent performance across validation and test data, and pose difficulty to select models on validation data during our experiments. FLDCRF offers easier model selection, consistency across validation and test performance and lucid model intuition. FLDCRF is also much faster to train compared to LSTM, even without a GPU. FLDCRF outshines the best LSTM model by ∼4% on a single-label task on UCI gesture phase data and outperforms LSTM performance by ∼2% on average across nested cross-validation test sets on the multi-label sequence tagging experiment on UCI opportunity data. The idea of FLDCRF can be extended to joint (multi-agent interactions) and heterogeneous (discrete and continuous) state space models.

Understanding the potential of emerging digital technologies for improving road safety

“…Machine learning was used to predict pedestrian behaviour at crossings [93] when mixed with cyclists [94] and walking speed respectively [95]. Of particular interest is the approach developed by Neogi and Dauwels [94] that was able to predict the pedestrian stopping 0.9 seconds before crossing the road in front of the vehicle. In comparison to the benchmarked system, their approach was able to detect the pedestrian stopping 0.52 seconds before the actual event.…”