Fast multiple instance learning via L1,2 logistic regression
Zhouyu Fu, Antonio Robles‐Kelly · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L1and L2regularisation techniques. An L1regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L1model efficiently, we employ a convex differentiable approximation of the L1cost function which can be solved by a quasi Newton method. We then train an L2regularised logistic regression model only on the subset of features with nonzero weights returned by the L1logistic regression. Experimental results demonstrate the utility and efficiency of the proposed approach compared to a number of alternatives.