Claims
- 1. A method, comprising:
processing a stream of data vectors; generating a visualization from a first group of the data vectors; determining a set of values corresponding to one or more eigenvectors for a matrix defined with the first group of the data vectors; and projecting each member of a second group of the data vectors onto the visualization as a function of the set of values.
- 2. The method of claim 1, which includes representing a number of text documents with the data vectors.
- 3. The method of claim 1, which includes representing a number of images with the data vectors.
- 4. The method of claim 1, wherein said generating includes performing a multidimensional scaling routine with the first group of the data vectors to generate the visualization in the form of a scatter plot.
- 5. The method of claim 1, wherein said projecting is performed in response to an increase in rate of receipt of the stream of the data vectors.
- 6. The method of claim 1, wherein said projecting includes determining a dot product of each member of the second group of the data vectors and at least one of the one or more eigenvectors.
- 7. The method of claim 1, which includes generating a data set representative of a portion of the stream of the data vectors by performing at least one of:
sampling the portion of the stream of data vectors; and reducing dimension of each of a plurality of the data vectors by wavelet decomposition.
- 8. A method, comprising:
receiving a first portion of a data stream at or below a defined rate; generating a first visualization from a group of data vectors corresponding to the first portion; receiving a second portion of the data stream above the defined rate; and generating a second visualization by updating the first visualization with one or more additional data vectors as a function of an eigenspace defined with the group of data vectors, the one or more additional data vectors corresponding to the second portion of the data stream.
- 9. The method of claim 8, wherein said generating includes determining a dot product between each of the one or more additional data vectors and one or more eigenvectors corresponding to the eigenspace.
- 10. The method of claim 8, which includes providing a reduced data set by performing at least one of a dimension reduction routine and a sampling routine with a number of data vectors.
- 11. The method of claim 10, which includes determining error of the second visualization with the reduced data set.
- 12. The method of claim 11, wherein said determining includes performing a procrustes similarity analysis.
- 13. The method of claim 10, which includes providing a third visualization based on the reduced data set.
- 14. The method of claim 8, wherein the data stream corresponds to at least one of: a number of text documents and a plurality of images.
- 15. The method of claim 8, wherein the first visualization and the second visualization each corresponding to a different scatter plot.
- 16. A method, comprising:
receiving a data stream; processing a group of data vectors corresponding to the data stream; generating a reduced data set which includes reducing dimension of the data vectors with wavelet decomposition; and providing a representation with the reduced data set corresponding to a visualization of a portion of the data stream.
- 17. The method of claim 16, which includes visualizing a part of the data stream in accordance with a multidimensional scaling routine.
- 18. The method of claim 16, which includes performing a similarity analysis with the representation.
- 19. The method of claim 16, wherein the data stream corresponds to at least one of: a number of text documents and a number of images.
- 20. The method of claim 16, wherein said generating includes performing the wavelet decomposition with Haar wavelets.
- 21. A method, comprising:
receiving a data stream; processing a stream of data vectors corresponding to the data stream; visualizing at least a portion of the data stream by executing a multidimensional scaling routine with at least a corresponding portion of the data vectors; and performing at least one of vector sampling and vector dimension reduction on a group of the data vectors to provide a data set with a reduced number of data elements relative to the group of the data vectors.
- 22. The method of claim 21, wherein the dimension reduction routine includes wavelet decomposition.
- 23. The method of claim 21, wherein the data stream includes at least one of: a number of text documents and a number of images.
- 24. The method of claim 21, which includes generating a visualization with the data set.
- 25. The method of claim 21, which includes performing a similarity analysis with the data set.
- 26. The method of claim 21, which includes updating a visualization provided by said visualizing with one or more additional data vectors as a function of one or more eigenvectors determined from the corresponding portion of the data vectors.
- 27. An apparatus, comprising: a device carrying logic executable by a processing subsystem receiving a data stream to generate a visualization from a group of data vectors corresponding to a first portion of the data stream received at a first rate and update the visualization as a function of one or more eigenvectors for the group of data vectors and one or more other data vectors corresponding to a second portion of the data stream received at a second rate greater than the first rate.
- 28. The apparatus of claim 27, wherein the logic is further operable to reduce dimension of another group of data vectors in accordance with a wavelet decomposition routine.
- 29. The apparatus of claim 27, wherein the device includes a computer accessible disk.
- 30. The apparatus of claim 27, wherein the device includes one or more components of a computer network.
- 31. A system, comprising:
a data communication subsystem operable to receive a data stream; a processing subsystem responsive to the data communication subsystem to generate a visualization output based on a group of data vectors corresponding to a first portion of the data stream, the processing subsystem being further responsive to a rate of receipt of the data stream to modify the visualization output with one or more other data vectors corresponding to a second portion of the data stream as a function of eigenspace defined with the group of data vectors; and a display device responsive to the visualization output to provide a corresponding visualization.
- 32. The system of claim 31, wherein the data processing subsystem is further operable to generate a reduced data set from the data stream with at least one of wavelet decomposition and vector sampling.
- 33. The system of claim 31, wherein the visualization output generated from the group of data vectors is provided in accordance with a multidimensional scaling routine executed by the data processing subsystem.
- 34. An apparatus, comprising:
means for processing a stream of data vectors and generating a visualization from a first group of the data vectors; means for determining a set of values corresponding to one or more eigenvectors for a matrix defined with the first group of the data vectors; and means for projecting each member of a second group of the data vectors onto the visualization as a function of the set of values.
- 35. An apparatus, comprising:
means for receiving a data stream; means for processing a group of data vectors corresponding to the data stream; means for generating a reduced data set which includes reducing dimension of the data vectors in accordance with a wavelet decomposition routine; and means for providing a visualization as a function of the reduced data set.
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] The present invention claims the benefit of U.S. Provisional Patent Application No. 60/459,841, filed 1 Apr. 2003, which is hereby incorporated by reference.
Provisional Applications (1)
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Number |
Date |
Country |
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60459841 |
Apr 2003 |
US |