摘要
Low-dimensional representation is a convenient method of obtaining a synthetic view of complex datasets and has been used in various domains for a long time. When the representation is related to words in a document, this kind of representation is also called a semantic map. The two most popular methods are self-organizing maps and generative topographic mapping. The second approach is statistically well-founded but far less computationally efficient than the first. On the other hand, a drawback of self-organizing maps is that they do not project all points, but only map nodes. This paper presents a method of obtaining the projections for all data points complementary to the self-organizing map nodes. The idea is to project points so that their initial distances to some cluster centers are as conserved as possible. The method is tested on an oil flow dataset and then applied to a large protein sequence dataset described by keywords. It has been integrated into an interactive data browser for biological databases.
Low-dimensional representation is a convenient method of obtaining a synthetic view of complex datasets and has been used in various domains for a long time. When the representation is related to words in a document, this kind of representation is also called a semantic map. The two most popular methods are self-organizing maps and generative topographic mapping. The second approach is statistically well-founded but far less computationally efficient than the first. On the other hand, a drawback of self-organizing maps is that they do not project all points, but only map nodes. This paper presents a method of obtaining the projections for all data points complementary to the self-organizing map nodes. The idea is to project points so that their initial distances to some cluster centers are as conserved as possible. The method is tested on an oil flow dataset and then applied to a large protein sequence dataset described by keywords. It has been integrated into an interactive data browser for biological databases.
