Techniques are disclosed for identifying and filtering outliers from a sample set of data prior to training a classifier on an object using the sample set. A data set including a plurality of samples used to train a classification model is retrieved. The samples in the data set have a feature dimensionality. A graph of the data set is built. Each node in the graph corresponds to a sample in the data set and edges connecting the nodes correspond to a measure of similarity between the nodes. The feature dimensionality of the sample data set is reduced based on a topology of the graph. One or more outliers in the data set are identified based on the reduced feature dimensionality.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: retrieving a data set including a plurality of samples used to train a classification model, wherein the samples in the data set have a feature dimensionality; building, by operation of a processor executing one or more applications, a graph data structure of the data set, wherein each node in the graph data structure corresponds to a sample in the data set and wherein an edge connecting a first and second node in the graph data structure corresponds to a measure of similarity between the first and second node; reducing the feature dimensionality of the sample data set based on a topology of the graph data structure; and identifying one or more outliers in the plurality of samples based on the reduced feature dimensionality. 2. The method of claim 1, further comprising: removing the identified outliers from the plurality of samples; and training a classifier on an object using the plurality of samples; 3. The method of claim 1, wherein the feature dimensionality is reduced using a transductive diffusion component analysis (TDCA) over the nodes of the graph data structure and wherein the outliers are identified using a pre-conditioned least absolute shrinkage and selection operator (LASSO). 4. The method of claim 1, wherein identifying the one or more outliers comprises: ordering the plurality of samples according to a likelihood that the sample is an outlier based on the reduced feature dimensionality. 5. The method of claim 1, wherein the classification model is trained to identify an object and wherein each outlier does not correspond to an instance of the object. 6. The method of claim 1, wherein the data set is retrieved from a web-based image service. 7. The method of claim 6, wherein the web-based image service labels each sample in the data set with an indication referencing an object. 8. A non-transitory computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation comprising: retrieving a data set including a plurality of samples used to train a classification model, wherein the samples in the data set have a feature dimensionality; building a graph data structure of the data set, wherein each node in the graph data structure corresponds to a sample in the data set and wherein an edge connecting a first and second node in the graph data structure corresponds to a measure of similarity between the first and second node; reducing the feature dimensionality of the sample data set based on a topology of the graph data structure; and identifying one or more outliers in the plurality of samples based on the reduced feature dimensionality. 9. The computer-readable storage medium of claim 8, wherein the operation further comprises: removing the identified outliers from the plurality of samples; and training a classifier on an object using the plurality of samples; 10. The computer-readable storage medium of claim 8, wherein the feature dimensionality is reduced using a transductive diffusion component analysis (TDCA) over the nodes of the graph data structure and wherein the outliers are identified using a pre-conditioned least absolute shrinkage and selection operator (LASSO). 11. The computer-readable storage medium of claim 8, wherein identifying the one or more outliers comprises: ordering the plurality of samples according to a likelihood that the sample is an outlier based on the reduced feature dimensionality. 12. The computer-readable storage medium of claim 8, wherein the classification model is trained to identify an object and wherein each outlier does not correspond to an instance of the object. 13. The computer-readable storage medium of claim 8, wherein the data set is retrieved from a web-based image service. 14. The computer-readable storage medium of claim 13, wherein the web-based image service labels each sample in the data set with an indication referencing an object. 15. A system, comprising: a processor; and a memory storing program code, which, when executed on the processor, performs an operation comprising: retrieving a data set including a plurality of samples used to train a classification model, wherein the samples in the data set have a feature dimensionality; building a graph data structure of the data set, wherein each node in the graph data structure corresponds to a sample in the data set and wherein an edge connecting a first and second node in the graph data structure corresponds to a measure of similarity between the first and second node; reducing the feature dimensionality of the sample data set based on a topology of the graph data structure; and identifying one or more outliers in the plurality of samples based on the reduced feature dimensionality. 16. The system of claim 15, wherein the operation further comprises: removing the identified outliers from the plurality of samples; and training a classifier on an object using the plurality of samples; 17. The system of claim 15, wherein the feature dimensionality is reduced using a transductive diffusion component analysis (TDCA) over the nodes of the graph data structure and wherein the outliers are identified using a pre-conditioned least absolute shrinkage and selection operator (LASSO). 18. The system of claim 15, wherein identifying the one or more outliers comprises: ordering the plurality of samples according to a likelihood that the sample is an outlier based on the reduced feature dimensionality. 19. The system of claim 15, wherein the classification model is trained to identify an object and wherein each outlier does not correspond to an instance of the object. 20. The system of claim 15, wherein the data set is retrieved from a web-based image service and wherein the web-based image service labels each sample in the data set with an indication referencing an object.