Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder
This is an incremental improvement for researchers in machine learning, offering a new linear variant of an existing nonlinear technique for supervised dimensionality reduction.
The authors tackled the problem of supervised dimensionality reduction by proposing Supervised Linear Centroid-Encoder (SLCE), a linear method that maps samples to class centroids, and demonstrated its performance advantage over other supervised methods in experiments.
We propose a new supervised dimensionality reduction technique called Supervised Linear Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder (CE) \citep{ghosh2022supervised}. SLCE works by mapping the samples of a class to its class centroid using a linear transformation. The transformation is a projection that reconstructs a point such that its distance from the corresponding class centroid, i.e., centroid-reconstruction loss, is minimized in the ambient space. We derive a closed-form solution using an eigendecomposition of a symmetric matrix. We did a detailed analysis and presented some crucial mathematical properties of the proposed approach. %We also provide an iterative solution approach based solving the optimization problem using a descent method. We establish a connection between the eigenvalues and the centroid-reconstruction loss. In contrast to Principal Component Analysis (PCA) which reconstructs a sample in the ambient space, the transformation of SLCE uses the instances of a class to rebuild the corresponding class centroid. Therefore the proposed method can be considered a form of supervised PCA. Experimental results show the performance advantage of SLCE over other supervised methods.