Dimensionality Reduction

1: Dimensionality reduction: Introduces the concept and need for reducing the complexity of highdimensional data in robotics.

2: Principal component analysis: Discusses PCA as a key linear technique for feature extraction and data compression.

3: Nonlinear dimensionality reduction: Explores nonlinear techniques for capturing complex data structures in robotics.

4: Eigenface: Covers the use of eigenfaces for facial recognition in robotics, demonstrating a realworld application of dimensionality reduction.

5: Empirical orthogonal functions: Describes a method for representing data in a way that captures important features for robotic systems.

6: Semidefinite embedding: Introduces this technique to preserve data relationships while reducing dimensionality, improving robotic data processing.

7: Linear discriminant analysis: Explains how LDA helps in classification tasks by focusing on class separability in reduced data.

8: Nonnegative matrix factorization: Describes how NMF helps in extracting partsbased representations from data, particularly for robotics.

9: Kernel principal component analysis: Expands on PCA with kernel methods to handle nonlinear data, crucial for robotics systems working with complex inputs.

10: Shogun (toolbox): Highlights the Shogun machine learning toolbox, which includes dimensionality reduction methods for robotic applications.

11: Spectral clustering: Covers this technique for clustering highdimensional data, an essential task in robotic perception and understanding.

12: Isomap: Discusses Isomap, a method for nonlinear dimensionality reduction, and its impact on improving robotic models.

13: Principal component regression: Links PCA with regression to reduce data dimensionality and improve predictive models in robotics.

14: Multilinear subspace learning: Introduces this advanced method for handling multidimensional data, boosting robot performance.

15: Mlpy: Details the Mlpy machine learning library, showcasing tools for dimensionality reduction in robotic systems.

16: Diffusion map: Focuses on the diffusion map technique for dimensionality reduction and its application to robotics.

17: Feature learning: Explores the concept of feature learning and its significance in enhancing robotic systems’ data interpretation.

18: Kernel adaptive filter: Discusses this filtering technique for adapting models to dynamic data, improving realtime robotic decisionmaking.

19: Random projection: Offers insights into how random projection techniques can speed up dimensionality reduction for large data sets in robotics.

20: Feature engineering: Introduces the process of designing features that help robots understand and interact with their environments more effectively.

21: Multivariate normal distribution: Concludes with an exploration of this statistical tool used in robotics for handling uncertainty and data modeling.