4 accepted papers
Multi-label data often contain high-dimensional features, outlier instances, and noisy labels, all of which can lead to the curse of dimensionality and decreased performance in downstream tasks. Although numerous data reduction methods have been developed, existing approaches face two major limitati
Multi-view unsupervised feature selection (MUFS) has received considerable attention in recent years. Existing MUFS methods for processing unlabeled incomplete multi-view data, where some samples are missing in certain views, first impute the missing values and then perform feature selection on the…
Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. These methods should first apply predetermined…
Human multimodal emotion recognition involves time-series data of different modalities, such as natural language, visual motions, and acoustic behaviors. Due to the variable sampling rates for sequences from different modalities, the collected multimodal streams are usually unaligned. The asynchrony…