Effective representation of data is crucial in various machine learning
tasks, as it captures the underlying structure and context of the data.
Embeddings have emerged as a powerful technique for data representation, but
evaluating their quality and capacity to preserve structural and contextual
information remains a challenge. In this paper, we address this need by
proposing a method to measure the \textit{representation capacity} of
embeddings. The motivation behind this work stems from the importance of
understanding the strengths and limitations of embeddings, enabling researchers
and practitioners to make informed decisions in selecting appropriate embedding
models for their specific applications. By combining extrinsic evaluation
methods, such as classification and clustering, with t-SNE-based neighborhood
analysis, such as neighborhood agreement and trustworthiness, we provide a
comprehensive assessment of the representation capacity. Additionally, the use
of optimization techniques (bayesian optimization) for weight optimization (for
classification, clustering, neighborhood agreement, and trustworthiness)
ensures an objective and data-driven approach in selecting the optimal
combination of metrics. The proposed method not only contributes to advancing
the field of embedding evaluation but also empowers researchers and
practitioners with a quantitative measure to assess the effectiveness of
embeddings in capturing structural and contextual information. For the
evaluation, we use $3$ real-world biological sequence (proteins and nucleotide)
datasets and performed representation capacity analysis of $4$ embedding
methods from the literature, namely Spike2Vec, Spaced $k$-mers, PWM2Vec, and
AutoEncoder.

通过结合分类、聚类和 t-SNE 基于邻近性分析的评估方法以及优化技术（如贝叶斯优化）来量化嵌入式数据在捕捉结构和上下文信息方面的有效性，本文旨在提出一种衡量嵌入式数据表现能力的方法，从而为研究者和实践者选择适用的嵌入式模型提供数据支持和辅助决策。