Described herein is a framework for outdoor localization. In accordance with one aspect of the framework, a set of hotspot labels are received from one or more user devices connected to an outdoor wireless local area network. Manifold learning may be performed based on the set of hotspot labels to construct one or more manifolds. Using the one or more constructed manifolds, the framework may then estimate a location of a particular user device associated with a query record received from during an online location query.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system for outdoor localization, comprising: a non-transitory memory device for storing computer-readable program code; and a processor in communication with the memory device, the processor being operative with the computer-readable program code to perform operations including receiving a set of hotspot labels from one or more user devices connected to an outdoor wireless local area network, preprocessing the set of hotspot labels by filtering out one or more labels in the set of hotspot labels, constructing a signal matrix for the preprocessed set of hotspot labels, wherein an entry in the signal matrix represents a Received Signal Strength Indicator for a given hotspot and a given label, performing manifold learning based on the signal matrix to construct one or more manifolds, and estimating, using the one or more constructed manifolds, locations of a particular user device and a hotspot associated with a query record received during an online location query. 2. The computer system of claim 1 wherein a crowdsensing application is installed on the one or more user devices to generate the set of hotspot labels. 3. The computer system of claim 1 wherein at least one of the hotspot labels includes, for a discovered hotpot, a Basic Service Set Identifier, a Service Set Identifier, a Received Signal Strength Indicator, a location, signal statistics or a combination thereof. 4. The computer system of claim 1 the processor is operative with the computer-readable program code to perform the manifold learning by performing a Laplacian Eigenmaps method. 5. A method of outdoor localization, comprising: receiving a set of hotspot labels from one or more user devices connected to an outdoor wireless local area network; performing manifold learning based on the set of hotspot labels to construct one or more manifolds; and estimating, using the one or more constructed manifolds, a location of a particular user device associated with a query record received during an online location query. 6. The method of claim 5, further comprising: initiating, by a crowdsensing application installed on the one or more user devices, a hotspot discovery process; recording the set of hotspot labels for hotspots discovered during the hotspot discovery process; and transmitting, by the crowdsensing application, the set of hotspot labels. 7. The method of claim 5, further comprising preprocessing the set of hotspot labels and performing the manifold learning on the preprocessed set of hotspot labels. 8. The method of claim 7, wherein preprocessing the set of hotspot labels comprises: removing, from the set of hotspot labels, labels with significant errors in location. 9. The method of claim 7, wherein preprocessing the set of hotspot labels comprises: removing, from the set of hotspot labels, labels that are determined to be mobile. 10. The method of claim 7, wherein preprocessing the set of hotspot labels comprises: combining, within the set of hotspot labels, labels with same or similar locations. 11. The method of claim 7, wherein preprocessing the set of hotspot labels comprises: constructing a signal matrix for the set of hotspot labels, wherein an entry in the signal matrix represents a Received Signal Strength Indicator (RSSI) for a given hotspot and a given label. 12. The method of claim 5 wherein performing the manifold learning comprises performing a Laplacian Eigenmaps method. 13. The method of claim 5 wherein performing the manifold learning comprises constructing a user device location manifold and a hotspot location manifold based on a signal matrix, wherein an entry in the signal matrix represents a Received Signal Strength Indicator (RSSI) for a given hotspot and a given label. 14. The method of claim 13 wherein performing the manifold learning further comprises: converting the RSSI to a weight using a non-linear transformation; and constructing weighted adjacency matrices for the user device location manifold and the hotspot location manifold based on the weight. 15. The method of claim 14 wherein the linear transformation comprises s ~ ij = exp  ( - ( s ij - s max ) 2 2  σ 2 ) wherein {tilde over (s)}ij represents the weight, sij represents the RSSI for a j-th hotspot in an i-th label, smax denotes a maximum RSSI a user device can receive in an outdoor environment, and σ denotes a Gaussian kernel width. 16. The method of claim 13 wherein estimating the location of the particular user device comprises: retrieving a subset of hotspots from the hotspot location manifold that are relevant to the query record; constructing a signal sub-matrix based on the subset of hotspots; constructing a hotspot location sub-manifold based on the signal sub-matrix and the hotspot location manifold; constructing a user device location sub-manifold based on the signal sub-matrix and the user device location manifold; and estimating the location of the particular user device based on the hotspot location sub-manifold and the user device location sub-manifold. 17. The method of claim 16, wherein estimating the location of the particular user device comprises: combining the hotspot location sub-manifold and the user device location sub-manifold into a unified sub-manifold; and finding an optimal location that minimizes fitting errors with known locations. 18. The method of claim 17 wherein finding the optimal location comprises determining p*=(K+γL)−1Ky wherein p* represents the optimal location, K represents an indication matrix, γ is a regularization term, L is a Laplacian graph, and y denotes the known locations. 19. One or more non-transitory computer-readable media having stored thereon program code, the program code executable by a computer to perform steps comprising: receiving a set of hotspot labels from one or more user devices connected to an outdoor wireless local area network; performing manifold learning based on the set of hotspot labels to construct one or more manifolds; and estimating, using the one or more constructed manifolds, a location of a particular user device associated with a query record received from during an online location query. 20. The one or more non-transitory computer-readable media of claim 19 wherein the query record comprises information associated with a list of one or more hotspots detected by a client application.