8,169 research outputs found
Adversarial Unsupervised Representation Learning for Activity Time-Series
Sufficient physical activity and restful sleep play a major role in the
prevention and cure of many chronic conditions. Being able to proactively
screen and monitor such chronic conditions would be a big step forward for
overall health. The rapid increase in the popularity of wearable devices
provides a significant new source, making it possible to track the user's
lifestyle real-time. In this paper, we propose a novel unsupervised
representation learning technique called activity2vec that learns and
"summarizes" the discrete-valued activity time-series. It learns the
representations with three components: (i) the co-occurrence and magnitude of
the activity levels in a time-segment, (ii) neighboring context of the
time-segment, and (iii) promoting subject-invariance with adversarial training.
We evaluate our method on four disorder prediction tasks using linear
classifiers. Empirical evaluation demonstrates that our proposed method scales
and performs better than many strong baselines. The adversarial regime helps
improve the generalizability of our representations by promoting subject
invariant features. We also show that using the representations at the level of
a day works the best since human activity is structured in terms of daily
routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with
arXiv:1712.0952
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
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