FAMER: Making Multi-Instance Learning Better and Faster

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 multiinstance 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 MultiinstancE 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