The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Self-taught learning: transfer learning from unlabeled data
Abstract
We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabeled data follows the same class labels or generative distribution as the labeled data. Thus, we would like to use a large number of unlabeled images (or audio samples, or text documents) randomly downloaded from the Internet to improve performance on a given image (or audio, or text) classification task. Such unlabeled data is significantly easier to obtain than in typical semi-supervised or transfer learning settings, making self-taught learning widely applicable to many practical learning problems. We describe an approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data. These features form a succinct input representation and significantly improve classification performance. When using an SVM for classification, we further show how a Fisher kernel can be learned for this representation.
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Cites
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A Global Geometric Framework for Nonlinear Dimensionality Reduction
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Text classification from labeled and unlabeled documents using EM
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Feature selection, L 1 vs. L 2 regularization, and rotational invariance
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Self-citation
Cited by
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Metadata
- Authors
- Raina, R., Battle, A., Lee, H., Packer, B., & Ng, A. Y.
- Venue
- Proceedings of the 24th international conference on Machine learning
- Publisher
- ACM
- Published
- 2007-06-20
- Pages
- 759-766
- OpenAlex
- W2122922389
- Cited by
- 1,510
- References
- 8
- FWCI
- 34.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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