Sensing-aware classification with high-dimensional data

In many applications decisions must be made about the state of an object based on indirect noisy observation of highdimensional data. An example is the determination of the presence or absence of stroke from tomographic projections. Conventionally, the sensing process is inverted and a classifier is built in the reconstructed domain, which requires complete knowledge of the sensing mechanism. Alternatively, a direct data domain classifier might be constructed, but the constraints imposed by the sensing process are then lost. In this work we study the behavior of a third path we term “sensingaware classification.” Our aim is to contribute to the development of a rigorous theory for such challenging problems. end, we consider an abstracted binary classification problem with very high dimensional observations, a restricting sensing configuration, and unknown statistical models of noise and object which must be learned from constrained training data. We analyze the impact of diff...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Burkay Orten, Prakash Ishwar, W. Clem Karl, Venkatesh Saligrama, Homer H. Pien
• Keywords: Abstracted Binary Classification, ICASSP 2011, Indirect Noisy Observation, Sensing, Signal Processing