Assigning topics to documents by successive projections
通过逐次投影为文档分配主题AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要聚焦文本主题模型与聚类，应用场景中的社交网络表述不足以支持网络与干扰等计量分类。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
通过逐次投影为文档分配主题
- 研究问题
- 如何从大型文本语料估计每篇文档的主题分布，实现允许主题重叠的聚类，并在主题数量未知时同时估计其数量？
- 方法与思路
- 作者借鉴可分离矩阵分解的逐次投影算法提出 SPOC，研究主题—文档矩阵估计，并扩展到主题数量未知的情形。
- 材料陈述的贡献
- 摘要称，算法实现简单且计算快速，具有性能上界和接近匹配的极小极大下界，并以合成及半合成数据开展数值研究。
- 阅读关注点
- 应核对模型与矩阵分解假设、误差度量、上下界匹配程度，以及未知主题数量时估计程序所需的额外条件。
- 阅读边界
- 仅依据版本未注明的 OpenAlex 索引摘要；摘要未列明理论条件、计算复杂度或数值结果，不能推断刊本变化。
原文摘要
Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering documents from big unstructured corpora into topics is an important task in various fields, such as image analysis, e-commerce, social networks and population genetics. Since the number of topics is typically substantially smaller than the size of the corpus and of the dictionary, the methods of topic modeling can lead to a dramatic dimension reduction. We study the problem of estimating the topic-document matrix, which gives the topics distribution for each document in a given corpus, that is, we focus on the clustering aspect of the problem. We introduce an algorithm that we call Successive Projection Overlapping Clustering (SPOC) inspired by the successive projection algorithm for separable matrix factorization. This algorithm is simple to implement and computationally fast. We establish upper bounds on the performance of the SPOC algorithm for estimation of the topic-document matrix, as well as near matching minimax lower bounds. We also propose a method that achieves analogous results when the number of topics is unknown and provides an estimate of the number of topics. Our theoretical results are complemented with a numerical study on synthetic and semisynthetic data.