Local Deep-Feature Alignment for Unsupervised Dimension Reduction (1904.09747v1)

Abstract: This paper presents an unsupervised deep-learning framework named Local Deep-Feature Alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local Stacked Contractive Auto-encoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.

Local Deep-Feature Alignment for Unsupervised Dimension Reduction

The paper "Local Deep-Feature Alignment for Unsupervised Dimension Reduction" by Jian Zhang, Jun Yu, and Dacheng Tao proposes an innovative approach to unsupervised dimension reduction using deep learning techniques. The authors introduce a framework named Local Deep-Feature Alignment (LDFA), which emphasizes capturing local deep characteristics and aligning them with global data representations.

Overview

LDFA is articulated around constructing neighborhoods for each data sample, where local characteristics are mined via a Stacked Contractive Auto-encoder (SCAE). This process isolates deep-level local features by stacking multiple auto-encoder layers, thereby reinforcing feature robustness. Subsequently, LDFA employs an affine transformation to align local deep features with global features, effectively encoding both local and global dependencies into the final low-dimensional representations.

Methodological Insights

• Local Feature Extraction: By employing SCAEs, LDFA captures localized data characteristics that are sensitive to manifold variations. This sensitivity is achieved through regularization which ensures robustness when training data size is limited.
• Alignment Procedure: The transition from local to global features is managed by affine transformations derived from local feature sets, akin to the principles of Local Tangent Space Alignment (LTSA). This provides a structured approach to capturing manifold structures while maintaining coherent global mapping.
• Explicit Mapping: The framework extends to accommodate new data samples, enabling embedding into the trained low-dimensional subspace. This is facilitated through the learned neural networks, providing an explicit mapping function for out-of-sample data points.

Experimental Evaluation

The authors execute extensive testing across various datasets, including MNIST Digits, USPS Digits, Olivetti Faces, UMist Faces, NABirds, Stanford Dogs, and Caltech-256, demonstrating LDFA’s competitive edge in data visualization, clustering, and classification tasks. These empirical results underscore the efficacy of locality exploitation in improving deep learning models.

Considerations and Implications

• Model Robustness: LDFA exhibits robustness, especially under reduced sample conditions, due to its emphasis on local feature extraction through SCAEs.
• Scalability: The framework's ability to handle varying datasets with substantial class diversity signifies its scalability for complex, real-world applications.
• Future Directions: LDFA’s architecture may inspire developments in adaptive learning mechanisms within AI, emphasizing local-global integration for nuanced feature learning.

Conclusion

The LDFA framework bridges the gap between local feature learning and global data alignment in unsupervised deep learning contexts. By addressing local characteristic preservation, LDFA stands as a versatile tool in the field of dimension reduction, promising further exploration and application in AI advancements.