## Line Chart: NDCG@10% vs Dimensions
### Overview
The chart compares the performance of four algorithms (PCA, Search-Adaptor, MRL-Adaptor, SMEC) in terms of NDCG@10% across increasing dimensions (128 to 768). All algorithms show upward trends, with SMEC and MRL-Adaptor achieving the highest performance at larger dimensions.
### Components/Axes
- **X-axis (Dimensions)**: Discrete values at 128, 256, 384, 512, 768.
- **Y-axis (NDCG@10%)**: Continuous scale from 12% to 24%.
- **Legend**: Located in the bottom-right corner, mapping colors/markers to algorithms:
- **Blue circles**: PCA
- **Orange crosses**: Search-Adaptor
- **Green triangles**: MRL-Adaptor
- **Yellow squares**: SMEC
### Detailed Analysis
1. **PCA (Blue Circles)**:
- Starts at ~13% at 128 dimensions.
- Increases steadily to ~18% at 768 dimensions.
- Slope: Gradual, linear growth.
2. **Search-Adaptor (Orange Crosses)**:
- Begins at ~18% at 128 dimensions.
- Rises sharply to ~22.5% at 768 dimensions.
- Slope: Steeper than PCA, nonlinear acceleration.
3. **MRL-Adaptor (Green Triangles)**:
- Starts at ~22% at 128 dimensions.
- Increases to ~24% at 768 dimensions.
- Slope: Consistent upward trend, minimal curvature.
4. **SMEC (Yellow Squares)**:
- Begins at ~22.5% at 128 dimensions.
- Reaches ~24% at 768 dimensions.
- Slope: Flat initially, then steep rise.
### Key Observations
- **Performance Trends**: All algorithms improve with higher dimensions, but SMEC and MRL-Adaptor dominate at larger scales.
- **Convergence**: MRL-Adaptor and SMEC nearly overlap at 768 dimensions (~24%).
- **PCA Lag**: PCA remains the lowest performer across all dimensions.
- **Search-Adaptor Acceleration**: Outperforms PCA by ~4.5% at 768 dimensions despite starting lower.
### Interpretation
The data suggests that increasing dimensionality generally enhances NDCG@10% for all algorithms, with SMEC and MRL-Adaptor being most effective. PCA’s lower performance may indicate suboptimal suitability for this task. The convergence of MRL-Adaptor and SMEC at higher dimensions implies they approach an optimal performance ceiling. Search-Adaptor’s steep rise highlights its scalability advantage over PCA. No outliers or anomalies are observed; trends align with expectations for dimensionality-driven performance gains.
