hyperspectral sensors provide a large number of spectral bands. this massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. we propose to use overlap-based feature weigh...

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...

Effective features can improve the performance of a model and help us understand characteristics underlying structure complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose novel supervised orthogonal least square regression with weighting for selection. The optimization problem objective functio...

This paper is a principal idea of case-based reasoning to feature weighting. The feature weighting method called CaDFeW (CAse-based Dynamic FEature Weighting) stores classification performance of randomly generated feature weight vectors. Also it retrieve similar feature weighting success story from the feature weighting case base and then designs a better feature weight vector dynamically for ...

This paper proposes the use of feature weights to reveal the hierarchical nature of music audio. Feature weighting has been exploited in machine learning, but has not been applied to music audio segmentation. We describe both a global and a local approach to automatic feature weighting. The global approach assigns a single weighting to all features in a song. The local approach uses the local s...

Feature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting...

transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. feature selection for transfer learning (f-mmd) is a simple and effective transfer learning method, which tackles the domain shift problem. f-mmd has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive perf...