Book Chapter10.1007/978-3-540-74377-4_110
Weighted Kernel Isomap for Data Visualization and Pattern Classification
Rui-jun Gu,Wen-bo Xu +1 more
- 01 Apr 2007
- pp 1050-1057
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TL;DR: WKIsomap is a supervised learning algorithm that can not only be used in data visualization, but also applied to feature extraction for pattern recognition and when noise is added into data, the WED based classifiers are more robust to noise than K isomap based ones.
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Abstract: Dimensionality reduction is an important task in pattern recognition and data mining. Isomap is a representative of manifold learning approaches for nonlinear dimensionality reduction. However, Isomap is an unsupervised learning algorithm and has no out-of-sample ability. Kernel Isomap (KIsomap) is an improved Isomap and has a generalization property by utilizing kernel trick. At first, considering class label, a Weighted Euclidean Distance (WED) is designed. Then, WED based kernel Isomap (WKIsomap) is proposed. As a supervised learning algorithm, WKIsomap can not only be used in data visualization, but also applied to feature extraction for pattern recognition. The experimental results show that WKIsomap is more robust than Isomap and KIsomap in data visualization. Moreover, when noise is added into data, WKIsomap based classifiers are more robust to noise than KIsomap based ones.
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Citations
•Journal Article
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
TL;DR: A rigorous definition for a specific visualization task is given, resulting in quantifiable goodness measures and new visualization methods, and it is shown empirically that the unsupervised version outperforms existing unsuper supervised dimensionality reduction methods in the visualization task, and the supervised version outper performs existing supervised methods.
Computational Intelligence and Security
Yue Hao,Jiming Liu,Yu-Ping Wang,Yiu-ming Cheung,Hujun Yin,Licheng Jiao,Jianfeng Ma,Yong-Chang Jiao +7 more
- 01 Jan 2005
TL;DR: A Novel Optimization Strategy for the Nonlinear Systems Identification and a New Schema Survival and Construction Theory for One-Point Crossover are discussed.
46
Classifier-based learning of nonlinear feature manifold for visualization of emotional speech prosody
TL;DR: A visualization method is presented that utilizes feature selection and classifier optimization for learning Isomap manifolds of emotional speech data based on those features that best discriminate between given emotional classes in the target space of specified embedding dimension.
30
Emotion recognition from speech using prosodic features
Eero Väyrynen
- 01 Jan 2014
TL;DR: Methods for emotion recognition from speech relying on long-term global prosodic parameters are developed, and Visualisation of emotional data congruent with the dimensional models of emotion is demonstrated utilising supervised nonlinear manifold modelling techniques.
Visualization of graphical data in a user-specified 2D space using a weighted Isomap method
Jong-Chul Yoon,In-Kwon Lee +1 more
TL;DR: A novel semi-supervised dimensionality reduction method is developed that can embed data of high dimension in a user-defined 2D coordinate system that is meaningful in terms of the properties of the data.
1
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