Making Online Sketching Hashing Even Faster

Abstract

Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However, they still suffer from several obstacles: First, most of existing hashing methods are trained in a batch mode, yielding inefficiency for training streaming data. Second, the computational cost and the memory consumption increase extraordinarily in the big data setting, which perplexes the training procedure. Third, the lack of labeled data hinders the improvement of the model performance. To address these difficulties, we utilize online sketching hashing (OSH) and present a FasteR Online Sketching Hashing (FROSH) algorithm to sketch the data in a more compact form via an independent transformation. We provide theoretical justification to guarantee that our proposed FROSH consumes less time and achieves a comparable sketching precision under the same memory cost of OSH. We also extend FROSH to its distribute

Stats

• citations16
• S2 citations—
• github stars0
• HF likes0
• heat score9.23
• arxiv keychen2020making

Related papers

• Hadamard Matrix Guided Online Hashing (2019)14.89
• Sketchmate: Deep Hashing For Million-scale Human Sketch Retrieval (2018)15.03
• Online Enhanced Semantic Hashing: Towards Effective And Efficient Retrieval For Streaming Multi-modal Data (2021)8.35
• Towards Optimal Discrete Online Hashing With Balanced Similarity (2019)12.17
• Supervised Online Hashing Via Similarity Distribution Learning (2019)0.00
• Scalable Discrete Supervised Hash Learning With Asymmetric Matrix Factorization (2016)4.52
• A Memory-efficient Sketch Method For Estimating High Similarities In Streaming Sets (2019)12.02
• Deep Sketch Hashing: Fast Free-hand Sketch-based Image Retrieval (2017)17.49