An Optimization Technique for Linear Manifold Learning-Based Dimensionality Reduction: Evaluations on Hyperspectral Images

Abstract

Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical especially for classification purposes. Locality preserving projection (LPP) and orthogonal locality preserving projection (OLPP) are two known linear manifold learning algorithms. In this study, scatter information of a distance matrix is used to construct a weight matrix with a supervised approach for the LPP and OLPP algorithms to improve classification accuracy rates. Low-dimensional data are classified with SVM and the results of the proposed method are compared with some other important existing linear manifold learning methods. Class-based enhancements and coefficients proposed for the formulization are reported visually. Furthermore, the change on weight matrices, band information, and correlation matrices with p-values are extracted and visualized to understand the effect of the proposed method. Experiments are conducted on hyperspectral imaging (HSI) with two different datasets. According to the experimental results, application of the proposed method with the LPP or OLPP algorithms outperformed traditional LPP, OLPP, neighborhood preserving embedding (NPE) and orthogonal neighborhood preserving embedding (ONPE) algorithms. Furthermore, the analytical findings on visualizations show consistency with obtained classification accuracy enhancements.

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

“…SLPP (Öztürk & Yılmaz, 2021) incorporate the sample category information into the adjacency relationship determination and the corresponding adjacency weight calculation. Specifically, SLPP considers the sample data points of same category as adjacent and assigns them corresponding adjacency weight of $$$ W=1/N $$$, where N is the number of sample points in this category.…”

“…SLPP (Öztürk & Yılmaz, 2021) Slime mold algorithm (SMA) (Li et al, 2020) imitates the foraging behavior of slime molds and the establishment of network structures.…”

Hyperspectral Band Selections for Enhancing the Discrimination of Difficult Targets Using Local Band Index and Particle Swarm Optimization

“…Its representative methods are Principal Component Analysis (PCA) [5,6] and Linear Discriminant Analysis (LDA) [7,8]. Local featurebased manifold learning [9,10] and sparse representation [11] are also important hyperspectral feature extraction methods. At the same time, feature extraction can also combine two different features of space and spectrum for DR [12].…”