Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network

no code implementations • 19 May 2017 • Zhenfang Hu, Gang Pan, Zhaohui Wu

The spectral graph theory provides us with a new insight into a fundamental aspect of classification: the tradeoff between fitting error and overfitting risk.

General Classification Model Selection +1

Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

no code implementations • 25 Mar 2014 • Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR).

Clustering Dimensionality Reduction

Sparse Principal Component Analysis via Rotation and Truncation

no code implementations • 6 Mar 2014 • Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation.