用球面分布优化时间序列哈希，提升语义检索效率。
Adaptive von Mises-Fisher Likelihood Loss for Supervised Deep Time Series Hashing
- 将数据映射到超球面空间，减少离散化信息损失。
- 基于von Mises-Fisher分布建模类别，提升不同语义样本的分离度。
- 适合需要高效语义搜索的时间序列应用，如医疗或金融时序分析。
通过生成紧凑的二进制表示来索引时间序列是时间序列数据挖掘中的基础任务。近年来，基于深度学习的哈希方法在根据语义而非仅原始相似性索引时间序列方面表现出色。深度哈希的目标是将具有相同语义意义的样本映射到相同的二进制哈希码，从而实现更高效的搜索与检索。与其他监督表示学习方法不同，监督深度哈希需要将实值表示离散化为二进制码，这一过程可能引入显著的信息损失。本文提出一种von Mises-Fisher（vMF）哈希损失。所提出的深度哈希模型将数据映射到M维超球面空间，有效减少信息损失，并将每个数据类别建模为遵循不同vMF分布的点。设计的损失函数旨在最大化各建模vMF分布之间的分离度，从而提供一种更优的方式以最大化语义不同的数据样本之间的间隔。实验结果表明，该方法优于现有基线。实现代码已公开于https://github.com/jmpq97/vmf-hashing。
原文摘要 · Abstract (English)
Indexing time series by creating compact binary representations is a fundamental task in time series data mining. Recently, deep learning-based hashing methods have proven effective for indexing time series based on semantic meaning rather than just raw similarity. The purpose of deep hashing is to map samples with the same semantic meaning to identical binary hash codes, enabling more efficient search and retrieval. Unlike other supervised representation learning methods, supervised deep hashing requires a discretization step to convert real-valued representations into binary codes, but this can induce significant information loss. In this paper, we propose a von Mises-Fisher (vMF) hashing loss. The proposed deep hashing model maps data to an M-dimensional hyperspherical space to effectively reduce information loss and models each data class as points following distinct vMF distributions. The designed loss aims to maximize the separation between each modeled vMF distribution to provide a better way to maximize the margin between each semantically different data sample. Experimental results show that our method outperforms existing baselines. The implementation is publicly available at https://github.com/jmpq97/vmf-hashing
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