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Challenges in Learning Optimum Models for Complex First Order Activity Recognition Settings

Non intrusive activity recognition systems typically read values from sensors deployed in an environment and combine them with user annotated activities to build a probabilistic model. Recently, features constructed from activity specific conjunctions of binary sensor values have been shown to improve the classification accuracy. Such systems employ greedy feature induction techniques to find the observation features and combine them with state transition distribution in a Hidden Markov Model or a Conditional Random Field. An exhaustive search for optimum features is infeasible in this exponential feature space. We have recently extended the rule ensemble learning using hierarchical kernels (RELHKL) framework, that learns a sparse set of simple features and their optimum weights, to structured output spaces for learning optimum observation features along with the transition features and their weights. The exponentially large space of conjunctions is handled efficiently by exploiting its hierarchical structure. Our experiments have shown good improvement over other approaches. Although such approaches solve propositional classification problems optimally, their first-order extension is non-trivial and is a challenging problem. In this paper, we discuss about the challenges involved in leveraging the RELHKL in structured output spaces approach to learn optimum features in complex first order activity recognition settings.