Multi-Instance Dimensionality Reduction

Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years. The curse of dimensionality also exists in multiinstance learning tasks, yet this difficult task has not been studied before. Direct application of existing single-instance dimensionality reduction objectives to multi-instance learning tasks may not work well since it ignores the characteristic of multi-instance learning that the labels of bags are known while the labels of instances are unknown. In this paper, we propose an effective model and develop an efficient algorithm to solve the multi-instance dimensionality reduction problem. We formulate the objective as an optimization problem by considering orthonormality and sparsity constraints in the projection matrix for dimensionality reduction, and then solve it by the gradient descent along...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Yu-Yin Sun, Michael K. Ng, Zhi-Hua Zhou
• Keywords: AAAI 2010, Dimensionality Reduction, Dimensionality Reduction Objectives, Intelligent Agents, Multi-instance Learning