LARGE-SCALE BATCH ACTIVE LEARNING USING LOCALITY SENSITIVE HASHING

摘要

A system and method for selection of a batch of objects are provided. Each object in a pool is assigned to a subset of a set of buckets. The assignment is based on signatures, generated, for example, by LSH hashing object representations of the objects in the pool. The signatures are then segmented into bands which are each assigned to a respective bucket in the set, based on the elements of the band. An entropy value is computed for each of a set of objects remaining in the pool using a current classifier model. A batch of objects for retraining the model is selected. This includes selecting objects from the set of objects based on their computed entropy values and respective assigned buckets.

主权项

1. A method for selection of a batch of objects comprising:
for each object in a pool of objects: performing Locality Sensitive Hashing on a multidimensional representation of the object to compute a signature comprising a sequence of elements; segmenting the signature into a plurality of bands, each band comprising a subset of the elements in the signature; assigning each of a plurality of bands of the signature to a respective one of a set of buckets based on values of the elements of the band; computing an entropy value for each of a set of objects remaining in the pool using a current classifier model; and selecting a batch of objects, including selecting objects from the set of objects based on their computed entropy values and respective assigned buckets, wherein at least one of the performing Locality Sensitive Hashing, segmenting the signature, assigning the bands, computing an entropy value, and selecting the batch of objects is performed with a processor.