منابع مشابه
Online Hashing
Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this paper proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss bet...
متن کاملFROSH: 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 requireme...
متن کاملMIHash: Online Hashing with Mutual Information
We discuss the implementation details of MIHash. In the online hashing experiments, for simplicity we model MIHash using linear hash functions, in the form of φi(x) = sgn(w> i x) ∈ {−1,+1}, i = 1, . . . , b. The learning capacity of such a model is lower than the kernel-based OKH, and is the same as OSH, AdaptHash, and SketchHash, which use linear hash functions as well. For the batch hashing e...
متن کاملOnline Supervised Hashing for Ever-Growing Datasets
Supervised hashing methods are widely-used for nearest neighbor search in computer vision applications. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies can be inefficient when confronted with large training datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as a dataset continues to grow and ...
متن کاملScalable Generalized Linear Bandits: Online Computation and Hashing
Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solutions to the GLB problem in two respects. First, unlike existing GLBs, whose per-timestep space and...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2018
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2017.2689242