Abstract. We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary's representative and discriminative power. In addition, we propose a probabilistic model over the sparse codes of input signals, which allows us to expand the labeled set. As a consequence, the dictionary and the classifier learned from the enlarged labeled set yield lower generalization error on unseen data. Our approach learns a single dictionary and a predictive linear classifier jointly. Experimental results demonstrate the effectiveness of our approach in face and object category recognition applications.

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A novel and simple technique for the measurement of time delay in the optical fiber by a free-running laser is proposed and demonstrated. The fiber to be measured was spliced to an erbium-doped fiber so as to form a ring-cavity laser. The mode beating frequency of the laser was measured to determine the round-trip time delay. This precise and efficient technique has an accuracy of 10(-8) for a fiber length of 100 km and of 10(-6) for a several-meters-long one.