Dynamic time-alignment k-means kernel clustering for time sequence clustering

This paper presents a novel method to cluster sequences by embedding a non-linear time alignment kernel function into kernel k-means. The time-alignment operation embeds the sequential pattern in the kernel function, allowing kernel k-means to be used to classify entire sequences. The method is evaluated with over 9800 videos and features from the LIRIS annotated creative commons emotional database. Our results show that the method works well in classifying sequences based on their affective content, and performs better than other unsupervised methods for clustering time series. In addition, this paper evaluates several methods abilities to map low-level features onto the valence arousal plane from the LIRIS database. The regression results also show that simple Ridge Regression had comparable performance to state-of-the-art regression methods.