Class Information Of Samples Research Articles

Dimensionality reduction and classification for hyperspectral image based on robust supervised ISOMAP

ABSTRACT This study contributes to classification and recognition of hyperspectral image in industrial sorting, mineral identification, geological exploration, environmental survey, and other industrial production fields. The finding eliminates the redundancy of hyperspectral data and improves the classification accuracy of hyperspectral image. Hyperspectral remote sensing data are inherently nonlinear, and traditional linear dimensionality reduction methods cannot effectively reveal the nonlinear structure contained in the data. Manifold learning is a nonlinear dimensionality reduction method which has been developed rapidly in recent years. The intrinsic structure of the data is preserved by Isometric Feature Mapping (ISOMAP), but it is sensitive to noise and is not conducive to final classification. Prior studies neglected a higher dimensional manifold real structure in hyperspectral data dimensionality reduction due to lack of consideration credibility information and class information of samples in calculating manifold distance. This study proposes a Robust Supervised ISOMAP method. Data sample credibility mode is constructed, and a data similarity measure named triple geodesic distance is defined by introducing samples’ credibility information, class information, and neighborhood information. Triple geodesic distance fitting is proven. Multidimensional scaling analysis (MDS) and generalized regression neural network are used to construct the embedded coordinates of training samples and test samples. The results show that the proposed method has higher anti-noise performance than the traditional one. Also, the runtime is feasible, and can effectively improve the classification precision of the hyperspectral images, in comparison to traditional ISOMAP methods. The conclusion is that Robust Supervised ISOMAP is an effective nonlinear dimensionality reduction method for hyperspectral remote sensing image.

Constrained discriminant neighborhood embedding for high dimensional data feature extraction

When handling pattern classification problem such as face recognition and digital handwriting identification, image data is always represented to high dimensional vectors, from which discriminant features are extracted using dimensionality reduction methods. So in this paper, we present a supervised manifold learning based dimensionality reduction method named constrained discirminant neighborhood embedding (CDNE). In the proposed CDNE, on one hand, the class information of samples is taken into account to construct both an inter-class graph and an intra-class graph, where neighborhood points in the intra-class graph are selected from those with the same class and any point in the inter-class graph should sample those labeled different classes as its neighborhood points. On the other hand, locally least linear reconstruction technique is also introduced to model an objective function under the local uncorrelation constraint to explore a discriminant subspace. Compared to some related and state-of-the-art dimensionality reduction methods such as discriminant neighborhood embedding (DNE), supervised locality discriminant manifold learning (SLDML), discriminant sparse neighborhood preserving embedding (DSNPE), local graph embedding based on maximum margin criterion (LGE/MMC), uncorrelated discriminant locality preserving projection (UDLPP) and locally uncorrelated discriminant projection (LUDP), the proposed CDNE has been validated to be efficient and feasible by experimental results on some benchmark face data sets including CMU PIE, ORL and FERET.

A theorem on the generalized canonical projective vectors

This paper proposes a kind of generalized canonical projective vectors (GCPV), based on the framework of canonical correlation analysis (CCA) applying image recognition. Apart from canonical projective vectors (CPV), the process of obtaining GCPV contains the class information of samples, such that the combined features extracted according to the basis of GCPV can give a better classification performance. The experimental result based on the Concordia University CENPARMI handwritten Arabian numeral database has proved that our method is superior to the method based on CPV.