821 research outputs found
Large margin metric learning for multi-label prediction
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods
Positional Encoding-based Resident Identification in Multi-resident Smart Homes
We propose a novel resident identification framework to identify residents in
a multi-occupant smart environment. The proposed framework employs a feature
extraction model based on the concepts of positional encoding. The feature
extraction model considers the locations of homes as a graph. We design a novel
algorithm to build such graphs from layout maps of smart environments. The
Node2Vec algorithm is used to transform the graph into high-dimensional node
embeddings. A Long Short-Term Memory (LSTM) model is introduced to predict the
identities of residents using temporal sequences of sensor events with the node
embeddings. Extensive experiments show that our proposed scheme effectively
identifies residents in a multi-occupant environment. Evaluation results on two
real-world datasets demonstrate that our proposed approach achieves 94.5% and
87.9% accuracy, respectively.Comment: 27 pages, 11 figures, 2 table
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