The best encoding is the one that is interpretable in nature. In this work, we introduce a novel model that incorporates an interpretable bottleneck-termed the Filter Bank (FB)-at the outset of a Variational Autoencoder (VAE). This arrangement compels the VAE to attend on the most informative segments of the input signal, fostering the learning of a novel encoding ${f_0}$ which boasts enhanced interpretability and clusterability over traditional latent spaces. By deliberately constraining the VAE with this FB, we intentionally constrict its capacity to access broad input domain information, promoting the development of an encoding that is discernible, separable, and of reduced dimensionality. The evolutionary learning trajectory of ${f_0}$ further manifests as a dynamic hierarchical tree, offering profound insights into cluster similarities. Additionally, for handling intricate data configurations, we propose a tailored decoder structure that is symmetrically aligned with FB's architecture. Empirical evaluations highlight the superior efficacy of ISVAE, which compares favorably to state-of-the-art results in clustering metrics across real-world datasets.

我们提出了一种新颖模型，它在变分自编码器（VAE）的开始处引入了一个可解释的瓶颈——称为滤波器组（FB）。这种安排迫使VAE注意输入信号中最有信息的部分，促进了一个具有增强解释性和集群性能的新编码$f_0$的学习，而与传统的潜在空间相比，它的维度较小。通过有意约束VAE的能力来访问广泛的输入域信息，我们故意限制了它与这个滤波器组的联系，从而促进了可辨别、可分离且维度较小的编码的发展。$f_0$的进化学习轨迹进一步表现为一个动态的分层树，为聚类相似性提供了深刻的见解。此外，为了处理复杂的数据配置，我们提出了一种与FB架构对称对齐的定制解码器结构。经验评估突出了ISVAE的优越效能，在现实世界的数据集中与最先进的聚类指标结果相比具有竞争力。

可解释的光谱变分自编码器（ISVAE）用于时间序列聚类