Similarity preservation in dimensionality reduction using a kernel-based cost function.
Sergio García-VegaGermán Castellanos-DomínguezPublished in: Pattern Recognit. Lett. (2019)
Keyphrases
- cost function
- dimensionality reduction
- kernel pca
- similarity measure
- euclidean distance
- multidimensional scaling
- kernel trick
- similarity measurement
- high dimensionality
- feature extraction
- data representation
- principal component analysis
- high dimensional data
- kernel discriminant analysis
- distance measure
- pattern recognition and machine learning
- objective function
- global minimum
- pattern recognition
- feature space
- high dimensional
- data points
- random projections
- multiple kernel learning
- expected cost
- kernel methods
- structure preserving
- edit distance
- input space
- kernel learning
- support vector
- graph embedding
- face recognition
- sparse kernel
- distance function