The present disclosure relates to a system, computer readable medium, and method for applying hyperdimensional computing and dimension reduction to extract properties from a sparse data set. Applying hyperdimensional computing can solve issues of dimensionality and dropout causing sparse data by expanding the dimension of the data. The result of hyperdimensional computing can involve too much data to be reasonably suitable for downstream computing processes (e.g., clustering for classification). Transforming the hyperdimensional embeddings provided by hyperdimensional computing into simplified/reduced embeddings can solve the problems of processing extremely large data. This improvement in accuracy and usefulness/useability of the sparse data helps reduce the need for extensive time, computing resources, and expensive equipment to extract expression data from deeper from cells.