Abstract
Now-a-days, signal processing remains an intensive challenging area of research. In fact, various strategies have been suggested to address semi-supervised, feature selection and unlabeled samples challenges. The most frequent achievement was dedicated to exploit a single kind of feature/view from the original data. Recently, advanced techniques aimed to explore signals from different views and to, properly, integrate divergent kinds of interdependent features. In this paper, we propose a novel design of a multi-View Graph Embedding for features selection allowing a convenient integration of complementary weighted features. The proposed framework combines the singular properties of each feature space to accomplish a physically meaningful cooperative low-dimensional selection of input data. This allows us not only to perform a semi-supervised classification, but also to propagates narrow class information to unlabeled sample when only partial labeling knowledge is available. This paper makes the following contributions: (i) a feature selection schema for data refinement; and (ii) the adaptation of a multi-view graph-based approach by a better tackling of semi-supervised and dimensionality issues. Our experimental results, conducted by using a mixture of complementary features and aerial images datasets, demonstrate the effectiveness of the proposed framework without significantly increasing computational complexity.
Highlights
In both signal processing/remote sensing community, a considerable growth has been shown in the plenty and the capacity of images which deliver a noticeable defiance to the traditional signal processing techniques [1]
The first problem is the computational complexity which will be increased by the size of the input space
The results show that the proposed approach is persuasive in remotely sensed image classification
Summary
In both signal processing/remote sensing community, a considerable growth has been shown in the plenty and the capacity of images which deliver a noticeable defiance to the traditional signal processing techniques [1]. The simple concatenation, without a proper modeling, will lead to some problems such as feature biasing [4] In other terms, this investigation confronts two major challenges. A more adapted modelling of extracted features allows a finest capture and modeling of the inherent structural information. To address this issue, we propose, in this work, a novel approach involving a graph based classification enhanced by a feature selection schema. A novel graph embedding schema is introduced for multi-view classification and an alternating model based on SSMF algorithm [8] is presented to efficiently optimize remote sensing signal classification.
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More From: International Journal of Advanced Computer Science and Applications
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