Interpretable Spectral Variational AutoEncoder (ISVAE) for time series
  clustering
Óscar Jiménez Rama Fernando Moreno-Pino David Ramírez Pablo M. Olmos
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.