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Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow-cytometric data analysis is performed through a series of 2-D projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma - cancers of the hematopoietic system. By only viewing a series of 2-D projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e., information distance) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just two at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow-cytometric research. We refer to our method as information preserving component analysis (IPCA).