DIPart
Domain Informed Partitioner
- DIPart is a domain informed feature extraction tool for low-dimensional data
- Generalized mixture model feature separator with automated self correction
- Use Cases: Compressed Sensing, Discrete Fourier Transform, Gaussian Mixture Model
- Significantly fewer parameters than competitive state-of-the-art AI methods
- Fully interpretable features with no post-hoc fine tuning
Practical Uses
Low Rank Extractor
Partition input data into user specified number of dominant interpretable features
Compressed Sensing
Jointly identify and select non-redundant dominant features
K+DFT
Extract user specified Discrete Fourier frequency basis with rank ordering
K+GMM
Extract user specified top rank-K Gaussian mixture modes including mean and variance
Automated Rejection
Self-corrects an inadvertently over-specified number of desired features
Resolving Interference
Extract interfering features. Coming Soon