Sparse coding with anomaly detection. Adler, A., Elad, M., Hel-Or, Y., & Rivlin, E. In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6, September, 2013. ISSN: 2378-928Xdoi abstract bibtex We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors - the outliers - which significantly deviate from this model. The proposed approach utilizes the Alternating Direction Method of Multipliers (ADMM) to recover simultaneously the sparse representations and the outliers components for the entire collection. This approach provides a unified solution both for jointly sparse and independently sparse data vectors. We demonstrate the usefulness of the proposed approach for irregular heartbeats detection in Electrocardiogram (ECG) and specular reflectance removal from natural images.
@inproceedings{adler_sparse_2013,
title = {Sparse coding with anomaly detection},
doi = {10.1109/MLSP.2013.6661898},
abstract = {We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors - the outliers - which significantly deviate from this model. The proposed approach utilizes the Alternating Direction Method of Multipliers (ADMM) to recover simultaneously the sparse representations and the outliers components for the entire collection. This approach provides a unified solution both for jointly sparse and independently sparse data vectors. We demonstrate the usefulness of the proposed approach for irregular heartbeats detection in Electrocardiogram (ECG) and specular reflectance removal from natural images.},
language = {en},
booktitle = {2013 {IEEE} {International} {Workshop} on {Machine} {Learning} for {Signal} {Processing} ({MLSP})},
author = {Adler, Amir and Elad, Michael and Hel-Or, Yacov and Rivlin, Ehud},
month = sep,
year = {2013},
note = {ISSN: 2378-928X},
keywords = {\#Deep Learning, \#Detection, \#Representation, \#Representation{\textgreater}Denoising, /unread, ADMM, Data models, Dictionaries, Electrocardiography, Encoding, Equations, Heart beat, Vectors, anomaly detection, arrythmia detection, sparse coding, specular reflectance removal, ❤️},
pages = {1--6},
}
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