BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
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发表时间：
2021
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通讯作者：
D. Danks;C. Yau
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作者：
D. Danks;C. Yau
变分自动编码器 (VAE) 在各种问题设置中执行有效的非线性降维。然而，通常采用的黑盒神经网络解码器函数限制了解码器函数被约束和解释的能力，使得在先验知识应嵌入到解码器中的设置中使用 VAE 会出现问题。我们提出了 DeVAE，一种基于 VAE 的新颖模型，具有基于导数的前向映射，允许通过导数空间中的解码器函数的指定来更好地控制解码器行为。此外，我们还展示了如何在创建 BasisDe-VAE 之前将 DeVAE 与稀疏聚类配对，并执行可解释的同步降维和特征级聚类。我们展示了 DeVAE 和 BasisDeVAE 模型在合成数据和真实数据上的性能和可扩展性，并展示了基于导数的方法如何实现尊重先验知识的富有表现力且可解释的正向模型。
The Variational Autoencoder (VAE) performs effective nonlinear dimensionality reduction in a variety of problem settings. However, the black-box neural network decoder function typically employed limits the ability of the decoder function to be constrained and interpreted, making the use of VAEs problematic in settings where prior knowledge should be embedded within the decoder. We present DeVAE, a novel VAE-based model with a derivative-based forward mapping, allowing for greater control over decoder behaviour via specifi-cation of the decoder function in derivative space. Additionally, we show how DeVAE can be paired with a sparse clustering prior to create BasisDe-VAE and perform interpretable simultaneous dimensionality reduction and feature-level clustering. We demonstrate the performance and scalability of the DeVAE and BasisDeVAE models on synthetic and real-world data and present how the derivative-based approach allows for expressive yet interpretable forward models which respect prior knowledge.