Learning Representations from Imperfect Time Series Data
via Tensor Rank Regularization
Abstract
There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment
analysis, and speech recognition. However,
naturally occurring multimodal data is often
imperfect as a result of imperfect modalities,
missing entries or noise corruption. To address these concerns, we present a regularization method based on tensor rank minimization. Our method is based on the observation
that high-dimensional multimodal time series
data often exhibit correlations across time and
modalities which leads to low-rank tensor representations. However, the presence of noise
or incomplete values breaks these correlations
and results in tensor representations of higher
rank. We design a model to learn such tensor representations and effectively regularize
their rank. Experiments on multimodal language data show that our model achieves good
results across various levels of imperfection