In this paper we study the problem of learning discriminative features (segments), often
referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series
data. Discovering shapelets for time series classification has been widely studied, where
many search-based algorithms are proposed to efficiently scan and select segments from a
pool of candidates. However, such types of search-based algorithms may incur high time
cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al …

In the Internet of Things (IoT), heterogenous sensors generate time-series data with different
properties. The problem of unsupervised feature learning from a time-series dataset poses
two challenges. First, it is known that centroids obtained by clustering time-series with high
overlap do not reflect their patterns, ie, subsequence time-series clustering is meaningless.
In this paper, we show that principal component analysis, sparse coding, and non-negative
matrix factorization are also meaningless for the same task, and that the systematic …