基于Logistic LDA的判别式主题建模
Discriminative Topic Modeling with Logistic LDA
摘要：
尽管对潜在狄利克雷分配（LDA）的研究已有多年，但将LDA应用于非分类项集合仍然具有挑战性。然而，许多具有更丰富数据的问题具有类似的结构，并且可以从大量的LDA文献中受益。我们提出了逻辑LDA，潜在的狄利克雷分配，这是很容易适用于任意输入的一种新的歧视性的变种。特别是，我们的模型可以很容易地应用于图像组，任意文本嵌入，并与深度神经网络很好地集成。虽然它是一个判别模型，但我们证明了logistic LDA可以通过利用数据中存在的组结构，以无监督的方式从未标记数据中学习。与其他最近的主题模型，旨在处理任意输入，我们的模型并没有牺牲LDA的可解释性和原则性的动机。
英文摘要：
Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging. Yet many problems with much richer data share a similar structure and could benefit from the vast literature on LDA. We propose logistic LDA, a novel discriminative variant of latent Dirichlet allocation which is easy to apply to arbitrary inputs. In particular, our model can easily be applied to groups of images, arbitrary text embeddings, and integrates well with deep neural networks. Although it is a discriminative model, we show that logistic LDA can learn from unlabeled data in an unsupervised manner by exploiting the group structure present in the data. In contrast to other recent topic models designed to handle arbitrary inputs, our model does not sacrifice the interpretability and principled motivation of LDA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
