Principal Component Classification
arXiv:2210.12746v21 citationsh-index: 23
Originality Synthesis-oriented
AI Analysis
This work addresses classification tasks, but appears incremental as it adapts PCA for supervised learning without major breakthroughs.
The authors tackled classification by learning features with class scores via PCA, resulting in a computationally efficient encoder-decoder model that performs well on several datasets.
We propose to directly compute classification estimates by learning features encoded with their class scores using PCA. Our resulting model has a encoder-decoder structure suitable for supervised learning, it is computationally efficient and performs well for classification on several datasets.