A highly accurate and fast power quality disturbances classification based on dictionary learning sparse decomposition.
Delong CaiKaicheng LiShunfan HeYuanzheng LiYi LuoPublished in: Trans. Inst. Meas. Control (2019)
Keyphrases
- dictionary learning
- highly accurate
- sparse representation
- sparse coding
- object category recognition
- power quality disturbance
- image classification
- sparsity constraints
- group sparsity
- signal processing
- sparse codes
- high accuracy
- pattern recognition
- decision trees
- linear combination
- active learning
- high quality
- discriminative dictionary
- pattern classification
- image patches
- generative model
- semi supervised
- high dimensional
- support vector
- face recognition