Recognition of Strong Earthquake Prone Areas in the Altai–Sayan–Baikal Region

With the development of feature extraction technique, one sample always can be represented by multiple features which locate in high-dimensional space. Multiple features can reflect various perspectives of one same sample, so there must be compatible and complementary information among the multiple views. Therefore, it's natural to integrate multiple features together to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reduction method named Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE reconstructs each sample by utilizing its k nearest neighbors. The similarities between each sample and its neighbors are primely mapped into lowerdimensional space in order to preserve the underlying neighborhood structure of the original manifold. MRPE fully exploits correlations between each sample and its neighbors from multiple views by linear reconstruction. Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding. Various evaluations based on the applications of document classification, face recognition and image retrieval demonstrate the effectiveness of our proposed approach on multi-view dimension reduction.

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Earthquake hazard and risk assessment based on unified scaling law for earthquakes: Altai–Sayan Region

Nekrasova

2018

Nat Hazards

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Recognition of Strong Earthquake Prone Areas in the Altai–Sayan–Baikal Region

Cited by 13 publications

References 3 publications

Pattern Recognition to Identify Susceptible Areas in Northwestern Himalaya

Pattern Recognition to Identify Susceptible Areas in Northwestern Himalaya

Multi-view reconstructive preserving embedding for dimension reduction

Earthquake hazard and risk assessment based on unified scaling law for earthquakes: Altai–Sayan Region

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