The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Incremental nonlinear dimensionality reduction by manifold learning
Abstract
Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition, and machine learning. Several algorithms have been proposed to analyze the structure of high-dimensional data based on the notion of manifold learning. These algorithms have been used to extract the intrinsic characteristics of different types of high-dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms operate in a "batch" mode and cannot be efficiently applied when data are collected sequentially. In this paper, we describe an incremental version of ISOMAP, one of the key manifold learning algorithms. Our experiments on synthetic data as well as real world images demonstrate that our modified algorithm can maintain an accurate low-dimensional representation of the data in an efficient manner.
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Feature dimensionality reduction: a review
Jia, W., Sun, M., Lian, J., & Hou, S. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00637-x · Cited by 797
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Object detection recognition and robot grasping based on machine learning: a survey
Bai, Q., Li, S., Yang, J., Song, Q., Li, Z., & Zhang, X. (2020) · IEEE Access · doi:10.1109/access.2020.3028740 · Cited by 154
Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery
Reutlinger, M., & Schneider, G. (2012) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2011.12.006 · Cited by 72
Metadata
- Authors
- Law, M. H. C., & Jain, A. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-03
- Volume
- 28
- Issue
- 3
- Pages
- 377-391
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2156761667
- Cited by
- 284
- References
- 46
- FWCI
- 22.35
- Type
- journal article
- Language
- en
- Open access
- closed
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