  The advancement of The Laser Interferometer Gravitational-Wave Observatory
(LIGO) has significantly enhanced the feasibility and reliability of
gravitational wave detection. However, LIGO's high sensitivity makes it
susceptible to transient noises known as glitches, which necessitate effective
differentiation from real gravitational wave signals. Traditional approaches
predominantly employ fully supervised or semi-supervised algorithms for the
task of glitch classification and clustering. In the future task of identifying
and classifying glitches across main and auxiliary channels, it is impractical
to build a dataset with manually labeled ground-truth. In addition, the
patterns of glitches can vary with time, generating new glitches without manual
labels. In response to this challenge, we introduce the Cross-Temporal
Spectrogram Autoencoder (CTSAE), a pioneering unsupervised method for the
dimensionality reduction and clustering of gravitational wave glitches. CTSAE
integrates a novel four-branch autoencoder with a hybrid of Convolutional
Neural Networks (CNN) and Vision Transformers (ViT). To further extract
features across multi-branches, we introduce a novel multi-branch fusion method
using the CLS (Class) token. Our model, trained and evaluated on the GravitySpy
O3 dataset on the main channel, demonstrates superior performance in clustering
tasks when compared to state-of-the-art semi-supervised learning methods. To
the best of our knowledge, CTSAE represents the first unsupervised approach
tailored specifically for clustering LIGO data, marking a significant step
forward in the field of gravitational wave research. The code of this paper is
available at https://github.com/Zod-L/CTSAE
