FAMER: Making Multi-Instance Learning Better and Faster

Wei Ping, Ye Xu, Jianyong Wang, Xian-Sheng Hua

Abstract: Kernel method is a powerful tool in multi-instance learning. However, many typical kernel methods for multi-instance learning ignore the correspondence information of instances between two bags or co-occurrence information, and result in poor performance. Additionally, most current multi-instance kernels unreasonably assign all instances in each bag an equal weight, which neglects the significance of some “key” instances in multi-instance learning. Last but not least, almost all the multi-instance kernels encounter a heavy computation load, which may fail in large datasets. To cope with these shortcomings, we propose a FAst kernel for Multi-instancE leaRning named as FAMER. FAMER constructs a Locally Sensitive Hashing (LSH) based similarity measure for multi-instance framework, and represents each bag as a histogram by embedding instances within the bag into an auxiliary space, which captures the correspondence information between two bags. By designing a bin-dependent weighting scheme, we not only impose different weights on instances according to their discriminative powers, but also exploit co-occurrence relations according to the joint statistics of instances. Without directly computing in a pairwise manner, the time complexity of FAMER is much smaller compared to other typical multi-instance kernels. The experiments demonstrate the effectiveness and efficiency of the proposed method.