FROSH: FasteR Online Sketching Hashing

Many hashing methods, especially those that are in the data-dependent category with good learning accuracy, are still inefficient when dealing with three critical problems in modern data analysis. First, data usually come in a streaming fashion, but most of the existing hashing methods are batch-based models. Second, when data become huge, the extensive computational time, large space requirement, and multiple passes to load the data into memory will be prohibitive. Third, data often lack sufficient label information. Although the recently proposed Online Sketching Hashing (OSH) is promising to alleviate all three issues mentioned above, its training procedure still suffers from a high time complexity. In this paper, we propose a FasteR Online Sketching Hashing (FROSH) method to make the training process faster. Compared with OSH, we leverage fast transform to sketch data more compactly. Particularly, we derive independent transformations to guarantee the sketching accuracy, and design a novel implementation to make such transformations applicable to online data sketching without increasing the space cost. We rigorously prove that our method can yield a comparable learning accuracy with a lower time complexity and an equal space cost compared with OSH. Finally, extensive experiments on synthetic and real-world datasets demonstrate the excellent performance of our method.