  Sequence data is challenging for machine learning approaches, because the
lengths of the sequences may vary between samples. In this paper, we present an
unsupervised learning model for sequence data, called the Integrated Sequence
Autoencoder (ISA), to learn a fixed-length vectorial representation by
minimizing the reconstruction error. Specifically, we propose to integrate two
classical mechanisms for sequence reconstruction which takes into account both
the global silhouette information and the local temporal dependencies.
Furthermore, we propose a stop feature that serves as a temporal stamp to guide
the reconstruction process, which results in a higher-quality representation.
The learned representation is able to effectively summarize not only the
apparent features, but also the underlying and high-level style information.
Take for example a speech sequence sample: our ISA model can not only recognize
the spoken text (apparent feature), but can also discriminate the speaker who
utters the audio (more high-level style). One promising application of the ISA
model is that it can be readily used in the semi-supervised learning scenario,
in which a large amount of unlabeled data is leveraged to extract high-quality
sequence representations and thus to improve the performance of the subsequent
supervised learning tasks on limited labeled data.

Computer Vision and Pattern Recognition;Artificial Intelligence