Working with high-dimensional data is inherently difficult due to its sparse distribution and structural complexity, which often obscure underlying patterns and reduce the effectiveness of visual analysis. Although technique like t-Distributed Stochastic Neighbor Embedding (t-SNE) is commonly used to map such data into lower dimensions while preserving local structures, their heavy computational demands—especially during initialization and when computing pairwise similarities—limit their use in applications that require fast or interactive performance. This paper presents a refined framework based on Approximated t-SNE (A-tSNE) to address these issues. The more efficient variant of A-tSNE employs approximate k-nearest neighbor (k-NN) searches. The refined A-tSNE is combined with sparsified graph construction based on estimates of local intrinsic dimensionality and a dynamic mechanism for detecting outliers. The Approximated Outlier Selection Factor (AOSF) is a key component of the method, which allows anomalous points to be identified and filtered out before generating the visual representation. Experimental validation on the MNIST and CIFAR-100 datasets reveals that this approach produces more precise and more informative visualizations by sharpening class boundaries, improving cluster separation, and preserving neighborhood consistency. These enhancements are further supported by quantitative assessments using trustworthiness and accuracy scores. The proposed method delivers a scalable, interactive, and outlier-aware visualization strategy that effectively balances computational efficiency with robust anomaly handling in high-dimensional data analysis.