Incremental nonlinear dimensionality reduction by manifold learning
By Martin H. C. Law, Anil K. Jain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
302 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/03e755815b69e3f37c2c9df6a1c6a8df7649706f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dacheng Tao
- Liangpei Zhang
- Hongyuan Zha
- Haiping Lu
- K. Plataniotis
- Anastasios N. Venetsanopoulos
- Sridhar Mahadevan
- Jian Lian
In Computer Vision and Pattern Recognition
- Liangpei Zhang
- Lefei Zhang
- Dacheng Tao
- Haiping Lu
- K. Plataniotis
- Anastasios N. Venetsanopoulos
- Xin Huang
- Hongyuan Zha
Citations in the corpus
- Feature dimensionality reduction: a review (2022, background): “Manifold is a term for general geometric objects, including curves and surfaces of various dimensions [89, 90].”
- On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (2012, methodology): “In this paper, the proposed multiple feature combining (MFC) is based on manifold learning [16], [17] and a patch alignment framework [18].”
- MPCA: Multilinear Principal Component Analysis of Tensor Objects (2008, background): “Feature extraction or dimensionality reduction is thus an attempt to transform a high-dimensional data set into a low-dimensional equivalent representation while retaining most of the information regarding the underlying structure or the actual physical phenomenon [18].”
- Online Learning: A Comprehensive Survey (2018, unknown): “For example, Law and Jain (2006) proposed an incremental learning algorithm for ISOMAP and Schuon et al. (2008) presented an online approach for LLE.”
- Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes (2007, methodology): “An incremental ISOMAP manifold learning technique has also recently been developed (Law and Jain, 2006).”
- Riemannian Manifold Learning (2008, background): “Note that for a manifold with complex topology, for example, a torus, multiple normal coordinate charts, rather than one single chart, are needed to cover the whole manifold.”
- Dimensionality Reduction: A Comparative Review (2008, unknown): “, Isomap [31,70], MVU [116,118], and Kernel PCA [108].”