论文标题
深发LDA
Deep generative LDA
论文作者
论文摘要
线性判别分析（LDA）是分类和降低维度的流行工具。但是，受其线性形式和基本高斯假设的限制，但是，LDA不适用于数据分布复杂的情况。最近，我们提出了一个歧视归一化流（DNF）模型。在这项研究中，我们将DNF重新解释为深层生成LDA模型，并研究其在表示复杂数据中的特性。我们进行了模拟实验和扬声器识别实验。结果表明，DNF及其子空间版本比传统的LDA强大得多，在建模复杂数据和检索低维表示。
Linear discriminant analysis (LDA) is a popular tool for classification and dimension reduction. Limited by its linear form and the underlying Gaussian assumption, however, LDA is not applicable in situations where the data distribution is complex. Recently, we proposed a discriminative normalization flow (DNF) model. In this study, we reinterpret DNF as a deep generative LDA model, and study its properties in representing complex data. We conducted a simulation experiment and a speaker recognition experiment. The results show that DNF and its subspace version are much more powerful than the conventional LDA in modeling complex data and retrieving low-dimensional representations.
