A method of detecting unknown classes is presented and includes generating a first classifier for multiple first classes. In one configuration, an output of the first classifier has a dimension of at least two. The method also includes designing a second classifier to receive the output of the first classifier to decide whether input data belongs to the multiple first classes or at least one second class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of detecting unknown classes, comprising: generating a first classifier for a first plurality of classes, an output of the first classifier having a dimension of at least two; and designing a second classifier to receive the output of the first classifier to decide whether input data belongs to the first plurality of classes or at least one second class. 2. The method of claim 1, further comprising classifying the input data into at least one unknown class when the input data does not belong to one of the first plurality of classes. 3. The method of claim 1, in which designing the second classifier comprises training the second classifier with examples of data belonging to the first plurality of classes and data not belonging to the first plurality of classes. 4. The method of claim 3, in which the data not belonging to the first plurality of classes comprises synthetically generated negative data. 5. The method of claim 4, in which the synthetically generated negative data is a function of known data from the first plurality of classes. 6. The method of claim 3, further comprising modifying a boundary of at least one of the first plurality of classes, one of the at least one second class, or a combination thereof based at least in part on the data not belonging to the first plurality of classes. 7. The method of claim 1, in which the first plurality of classes are a plurality of known classes. 8. The method of claim 1, in which the at least one second class comprises an unknown class or a plurality of classes that are different from the first plurality of classes. 9. The method of claim 1, in which the second classifier is linear or non-linear. 10. A method of generating synthetic negative data, comprising: obtaining known data from a plurality of classes; and synthetically generating negative data as a function of the known data. 11. The method of claim 10, in which synthetically generating the negative data comprises: computing a first vector between each known data point in a cluster of known data and a centroid of the cluster; and computing a second vector between a centroid of class specific clusters and a centroid of all known data points independent of class. 12. The method of claim 11, further comprising generating the negative data from the second vector or a negative vector of the first vector. 13. The method of claim 10, further comprising training a classifier on the negative data. 14. The method of claim 10, further comprising modifying a boundary of at least an existing known class, an existing unknown class, or a combination thereof based at least in part on the negative data. 15. An apparatus for detecting unknown classes, comprising: at least one memory unit; and at least one processor coupled to the memory unit, the at least one processor configured: to generate a first classifier for a first plurality of classes, an output of the first classifier having a dimension of at least two; and to design a second classifier to receive the output of the first classifier to decide whether input data belongs to the first plurality of classes or at least one second class. 16. The apparatus of claim 15, in which the at least one processor is further configured to classify the input data into at least one unknown class when the input data does not belong to one of the first plurality of classes. 17. The apparatus of claim 15, in which the at least one processor is further configured to train the second classifier with examples of data belonging to the first plurality of classes and data not belonging to the first plurality of classes. 18. The apparatus of claim 17, in which the data not belonging to the first plurality of classes comprises synthetically generated negative data. 19. The apparatus of claim 18, in which the synthetically generated negative data is a function of known data from the first plurality of classes. 20. The apparatus of claim 17, in which the at least one processor is further configured to modify a boundary of at least one of the first plurality of classes, one of the at least one second class, or a combination thereof based at least in part on the data not belonging to the first plurality of classes. 21. The apparatus of claim 15, in which the first plurality of classes are a plurality of known classes. 22. The apparatus of claim 15, in which the at least one second class comprises an unknown class or a plurality of classes that are different from the first plurality of classes. 23. The apparatus of claim 15, in which the second classifier is linear or non-linear. 24. A apparatus for generating synthetic negative data, comprising: at least one memory unit; and at least one processor coupled to the memory unit, the at least one processor configured: to obtain known data from a plurality of classes; and to synthetically generate negative data as a function of the known data. 25. The apparatus of claim 24, in which the at least one processor is further configured: to compute a first vector between each known data point in a cluster of known data and a centroid of the cluster; and to compute a second vector between a centroid of class specific clusters and a centroid of all known data points independent of class. 26. The apparatus of claim 25, in which the at least one processor is further configured to generate the negative data from the second vector or a negative vector of the first vector. 27. The apparatus of claim 24, in which the at least one processor is further configured to train a classifier on the negative data. 28. The apparatus of claim 24, in which the at least one processor is further configured to modify a boundary of at least an existing known class, an existing unknown class, or a combination thereof based at least in part on the negative data. 29. A non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to generate a first classifier for a first plurality of classes, an output of the first classifier having a dimension of at least two; and program code to design a second classifier to receive the output of the first classifier to decide whether input data belongs to the first plurality of classes or at least one second class. 30. A non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to obtain known data from a plurality of classes; and program code to synthetically generate negative data as a function of the known data. 31. An apparatus for detecting unknown classes, comprising: means for generating a first classifier for a first plurality of classes, an output of the first classifier having a dimension of at least two; and means for designing a second classifier to receive the output of the first classifier to decide whether input data belongs to the first plurality of classes or at least one second class. 32. An apparatus for generating synthetic negative data, comprising: means for obtaining known data from a plurality of classes; and means for synthetically generating negative data as a function of the known data.