Plant leaf classification based on weighted locally linear embedding

Locally linear embedding (LLE) is effective in discovering the geometrical structure of the data. But when it is applied to real-world data, it shows some weak points, such as being quite sensitive to noise points and outliers, and being unsupervised in nature. In this paper, we propose a weighted LLE. The experiments on synthetic data and real plant leaf data demonstrate that the proposed algorithm can efficiently maintain an accurate low-dimensional representation of the noisy manifold data with less distortion, and acquire higher average recognition rates of plant leaf compared to other dimensional reduction methods.