图 书 馆
基于优化核散列的可扩展相似性搜索Scalable Similarity Search with Optimized Kernel Hashing |
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| 课程网址: | http://videolectures.net/kdd2010_he_sss/ |
| 主讲教师: | Junfeng He |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Scalable similarity search is the core of many large scale learning or data mining applications. Recently, many research results demonstrate that one promising approach is creating compact and efficient hash codes that preserve data similarity. By efficient, we refer to the low correlation (and thus low redundancy) among generated codes. However, most existing hash methods are designed only for vector data. In this paper, we develop a new hashing algorithm to create efficient codes for large scale data of general formats with any kernel function, including kernels on vectors, graphs, sequences, sets and so on. Starting with the idea analogous to spectral hashing, novel formulations and solutions are proposed such that a kernel based hash function can be explicitly represented and optimized, and directly applied to compute compact hash codes for new samples of general formats. Moreover, we incorporate efficient techniques, such as Nystrom approximation, to further reduce time and space complexity for indexing and search, making our algorithm scalable to huge data sets. Another important advantage of our method is the ability to handle diverse types of similarities according to actual task requirements, including both feature similarities and semantic similarities like label consistency. We evaluate our method using both vector and non-vector data sets at a large scale up to 1 million samples. Our comprehensive results show the proposed method outperforms several state-of-the-art approaches for all the tasks, with a significant gain for most tasks. |
| 关 键 词: | 哈希方法; 内核函数; 紧凑散列码 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：lxf |
| 阅读次数: | 61 |