A data processing apparatus that processes a spectral data item which stores, for each of a plurality of spectral components, an intensity value, includes a spectral component selecting unit and a classifier generating unit. The spectral component selecting unit is configured to select, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items. The classifier generating unit is configured to perform machine learning by using the plurality of machine-learning spectral components selected by the spectral component selecting unit and generate a classifier that classifies a spectral data item.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data processing apparatus that processes a spectral data item which stores, for each of a plurality of spectral components, an intensity value, comprising: a spectral component selecting unit configured to select, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items; and a classifier generating unit configured to perform machine learning by using the plurality of machine-learning spectral components selected by the spectral component selecting unit and generate a classifier that classifies a spectral data item. 2. The data processing apparatus according to claim 1, wherein the spectral component selecting unit selects the plurality of machine-learning spectral components in order of decreasing Mahalanobis distance. 3. The data processing apparatus according to claim 1, wherein the spectral component selecting unit selects the machine-learning spectral components in order of decreasing Mahalanobis distance separately for each of a plurality of combinations of the groups to be distinguished by the classifier. 4. The data processing apparatus according to claim 1, wherein the spectral component selecting unit selects the plurality of machine-learning spectral components finely at a part where the Mahalanobis distance is large and coarsely at a part where the Mahalanobis distance is small. 5. (canceled) 6. The data processing apparatus according to claim 1, wherein the spectral data items are spectral data items stored for respective pixels in image data. 7. The data processing apparatus according to claim 1, wherein the classifier generating unit performs, for each of the plurality of machine-learning spectral components, an intensity value averaging process in accordance with magnitude of a within-group variance of the plurality of spectral data items and performs machine learning. 8. The data processing apparatus according to claim 1, wherein the spectral data items are spectral data items including any one of spectral data items obtained by ultraviolet, visible, or infrared spectroscopy, spectral data items obtained by Raman spectroscopy, and mass spectral data items. 9. The data processing apparatus according to claim 1, wherein the spectral components are represented by a wave number or a mass-to-charge ratio. 10. The data processing apparatus according to claim 1, further comprising: a classifying unit configured to classify a spectral data item by using the classifier generated by the classifier generating unit. 11. The data processing apparatus according to claim 10, wherein two-dimensional image data is generated based on a classification result obtained by the classifying unit, the two-dimensional image data being data for distinguishably displaying pixels for which respective spectral data items are stored. 12-13. (canceled) 14. A sample information obtaining system comprising: the data processing apparatus according to claim 1; and a measuring unit configured to perform measurement on a sample to obtain the spectral data items. 15. The sample information obtaining system according to claim 14, wherein the measuring unit performs measurement on the basis of the machine-learning spectral components selected by the spectral component selecting unit to obtain the spectral data items. 16. A data processing method for processing a spectral data item which stores, for each of a plurality of spectral components, an intensity value, comprising: selecting, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items; and performing machine learning by using the plurality of machine-learning spectral components selected in the selecting, and generating a classifier that classifies a spectral data item. 17. The data processing method according to claim 16, further comprising: classifying a spectral data item by using the generated classifier. 18. (canceled) 19. A computer-readable storage medium storing a program causing a computer to execute a process, the process comprising: selecting, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among a plurality of spectral components of the plurality of spectral data items each storing, for each of the plurality of spectral components, an intensity value; and performing machine learning by using the plurality of machine-learning spectral components selected in the selecting and generating a classifier that classifies a spectral data item.