DGPAGE: DGPAGE: Discriminative and Geometry-Preserving Adaptive Graph...

In bbuchsbaum/discursive: What the Package Does (One Line, Title Case)

DGPAGE: Discriminative and Geometry-Preserving Adaptive Graph Embedding

Description

This package-like script provides a complete, faithful, working out-of-the-box R implementation of the DGPAGE method described in:

Details

J. Gou, X. Yuan, Y. Xue, L. Du, J. Yu, S. Xia, Y. Zhang."Discriminative and Geometry-Preserving Adaptive Graph Embedding for dimensionality reduction."Information Sciences, 2022.

DGPAGE systematically integrates:

1. Manually constructed similarity and diversity graphs (see Eqs. (4) and (7) in the paper),

2. An adaptively learned adjacency graph with diversity-based regularization (Eqs. (8)-(11)),

3. A multi-task-like objective that jointly optimizes the adjacency matrix and the projection matrix (Eq. (12)), and

4. An alternating optimization algorithm (Algorithm 1 in the paper) that solves for these parameters.

The main user-facing functions are:

• dgpage_fit(): fits the DGPAGE model on training data,

• dgpage_predict(): obtains predictions (via 1-NN) on new test data,

• compute_similarity_graph(): constructs the manual similarity graph G_S (Eq. (4)),

• compute_diversity_graph(): constructs the manual diversity graph G_D (Eq. (7)).

The code below relies on base R (for matrix operations) and uses the Rnanoflann package for 1-nearest-neighbor classification.

Note: For large datasets n, the O(n^2) graph construction may be expensive in plain R. You might want to accelerate via Rcpp or other means.

Author(s)

• Original Paper: Jianping Gou, Xia Yuan, Ya Xue, Lan Du, Jiali Yu, Shuyin Xia, Yi Zhang

• R Implementation: Your Name ([e-mail])

References

• Gou, J., Yuan, X., Xue, Y., Du, L., Yu, J., Xia, S., & Zhang, Y. (2022). Discriminative and Geometry-Preserving Adaptive Graph Embedding for dimensionality reduction. Information Sciences.

bbuchsbaum/discursive documentation built on April 14, 2025, 4:57 p.m.