Abstract
In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturally gives relative importance to the labeled ones through a graph-based methodology. Then it tries to extract discriminative features on each manifold such that the data points in the same manifold become closer. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated and compared through experiments on a real hyperspectral images.
Highlights
Hyperspectral image (HSI) contains dozens or even hundreds of contiguous spectral bands, which has been widely used in land cover investigation [1]
The goal of the experiments is to investigate the effectiveness of the proposed algorithms for classification of PaviaU hyperspectral data set
For supervised dimensionality reduction (DR) methods, only the labeled set is used for training, while semi-supervised DR methods can utilize both labeled and unlabeled data
Summary
Hyperspectral image (HSI) contains dozens or even hundreds of contiguous spectral bands, which has been widely used in land cover investigation [1]. The representative methods include sparse preserving projection (SPP) [3], discriminative learning by sparse representation (DLSP) [4] and discriminant sparse neighborhood preserving embedding (DSNPE) [5] These mentioned works implicitly assume that data points uniformly lie on a single manifold. S3MME utilizes the merits of both sparsity property and multimanifold learning to better characterize the discriminant property of the data It exploits both the labeled and unlabeled pixels to adaptively find the local neighborhood of each data point by using an optimization program based on SR, and the selected neighbors are from the same manifold other than other manifolds. The weights associated to the chosen neighbors are automatically obtained simultaneously It exploits the wealth of labeled samples in HSI data, and naturally gives relative importance to the labeled ones following a semi-supervised approach. An objective function pushes the homogeneous samples closer to each other is proposed, and the classification performance is further improved
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