The Curse of Dimensionality in Data Mining and Time Series Prediction

By Michel Verleysen, Damien François. International Work-Conference on Artificial and Natural Neural Networks, 2005

783 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d9020d562d03ee3d1c3aebda1a46a711aeb64a2a

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

1. Yutao Huang
2. QUAN LIU
3. Jian Pei
4. Zhiqiang Guo
5. Arman Malekloo
6. Jugdutt Singh
7. Jie Yang
8. Jiangchuan Liu

In Economics and Econometrics

1. QUAN LIU
2. Jian Pei
3. Zhiqiang Guo
4. Jie Yang
5. Antti Sorjamaa
6. Mark A. Girolami
7. Huaiqing Wang

In Signal Processing

1. Miguel A. L. Nicolelis
2. QUAN LIU
3. Jian Pei
4. Zhiqiang Guo
5. Jugdutt Singh
6. Jie Yang
7. Stephan K. Chalup
8. Philip Branch

Citations in the corpus

• A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, methodology): “However, in feature extraction, the dimension can be decreased without losing much initial feature dataset [2, 10, 14, 17].”
• Personalized Cross-Silo Federated Learning on Non-IID Data (2020, background): “In this case, Euclidean distance may not be effective in evaluating the difference between wi and wj anymore due to the curse of dimensionality (Verleysen and François 2005).”
• Overview and comparative study of dimensionality reduction techniques for high dimensional data (2020, neutral): “Another common issue of HDD is curse of dimensionality [23] .”
• Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “known in many disciplines as overfitting (38, 338, 345) or the curse of dimensionality (821).”
• A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “The first difference between ICA and PCA is that the components of PCA are orthogonal, while those of ICA are independent.”
• Methodology for long-term prediction of time series (2007, background): “The direct strategy increases the complexity of the prediction, but more accurate results are achieved as illustrated in Section 5.”
• A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, background): “However, when the projection of dimensionality reduction fails, it is impossible to visualize the data in detail and it can only be comprehended mathematically [57].”
• Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “98 Data compression should not come at the cost of losing the ability to learn a pattern.”
• Kernel Methods in Finance (2008, background): “The reason for this is that high-dimensional spaces have a few, sometimes counter-intuitive, properties which can have essential impact on kernel choice or design (Verleysen and Francois, 2005).”
• A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction (2016, background): “It is easy to prove that the number of learning data should grow exponentially with the dimension of feature space [27].”