Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation

TL;DR

This paper introduces Stochastic Coordinate Coding, a new efficient algorithm for sparse coding that enhances feature extraction from large-scale gene expression images in Drosophila, aiding automated annotation.

Contribution

The paper presents a novel stochastic coordinate coding algorithm that improves computational efficiency and scalability for sparse coding in large datasets.

Findings

The algorithm significantly reduces computational cost.

It effectively extracts features from large-scale gene expression images.

Experiments demonstrate improved efficiency and effectiveness.

Abstract

\textit{Drosophila melanogaster} has been established as a model organism for investigating the fundamental principles of developmental gene interactions. The gene expression patterns of \textit{Drosophila melanogaster} can be documented as digital images, which are annotated with anatomical ontology terms to facilitate pattern discovery and comparison. The automated annotation of gene expression pattern images has received increasing attention due to the recent expansion of the image database. The effectiveness of gene expression pattern annotation relies on the quality of feature representation. Previous studies have demonstrated that sparse coding is effective for extracting features from gene expression images. However, solving sparse coding remains a computationally challenging problem, especially when dealing with large-scale data sets and learning large size dictionaries. In this paper, we propose a novel algorithm to solve the sparse coding problem, called Stochastic Coordinate Coding (SCC). The proposed algorithm alternatively updates the sparse codes via just a few steps of coordinate descent and updates the dictionary via second order stochastic gradient descent. The computational cost is further reduced by focusing on the non-zero components of the sparse codes and the corresponding columns of the dictionary only in the updating procedure. Thus, the proposed algorithm significantly improves the efficiency and the scalability, making sparse coding applicable for large-scale data sets and large dictionary sizes. Our experiments on Drosophila gene expression data sets demonstrate the efficiency and the effectiveness of the proposed algorithm.