Multi-view Sparse Laplacian Eigenmaps for nonlinear Spectral Feature
  Selection
Gaurav Srivastava Mahesh Jangid
The complexity of high-dimensional datasets presents significant challenges
for machine learning models, including overfitting, computational complexity,
and difficulties in interpreting results. To address these challenges, it is
essential to identify an informative subset of features that captures the
essential structure of the data. In this study, the authors propose Multi-view
Sparse Laplacian Eigenmaps (MSLE) for feature selection, which effectively
combines multiple views of the data, enforces sparsity constraints, and employs
a scalable optimization algorithm to identify a subset of features that capture
the fundamental data structure. MSLE is a graph-based approach that leverages
multiple views of the data to construct a more robust and informative
representation of high-dimensional data. The method applies sparse
eigendecomposition to reduce the dimensionality of the data, yielding a reduced
feature set. The optimization problem is solved using an iterative algorithm
alternating between updating the sparse coefficients and the Laplacian graph
matrix. The sparse coefficients are updated using a soft-thresholding operator,
while the graph Laplacian matrix is updated using the normalized graph
Laplacian. To evaluate the performance of the MSLE technique, the authors
conducted experiments on the UCI-HAR dataset, which comprises 561 features, and
reduced the feature space by 10 to 90%. Our results demonstrate that even after
reducing the feature space by 90%, the Support Vector Machine (SVM) maintains
an error rate of 2.72%. Moreover, the authors observe that the SVM exhibits an
accuracy of 96.69% with an 80% reduction in the overall feature space.