Abstract: The disclosed computer-implemented method may include collecting a set of labels that label polygons within a training set of images as architectural structures. The method may also include creating a set of noisy labels with a predetermined degree of noise by distorting boundaries of a number of the polygons within the training set of images. Additionally, the method may include simultaneously training two neural networks by applying a co-teaching method to learn from the set of noisy labels. The method may also include extracting a preferential list of training data based on the two trained neural networks. Furthermore, the method may include training a machine learning model with the preferential list of training data. Finally, the method may include identifying one or more building footprints in a target image using the trained machine learning model. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include collecting a set of labels that label polygons within a training set of images as architectural structures. The method may also include creating a set of noisy labels with a predetermined degree of noise by distorting boundaries of a number of the polygons within the training set of images. Additionally, the method may include simultaneously training two neural networks by applying a co-teaching method to learn from the set of noisy labels. The method may also include extracting a preferential list of training data based on the two trained neural networks. Furthermore, the method may include training a machine learning model with the preferential list of training data. Finally, the method may include identifying one or more building footprints in a target image using the trained machine learning model. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Abstract: A computing device determines a sparse feature representation for a machine learning model. Landmark observation vectors are randomly selected. Neighbor observation vectors are randomly selected that are less than a predefined distance from a selected landmark observation vector. The observation vectors are projected into a neighborhood subspace defined by principal components computed for the neighbor observation vectors. A distance vector includes a distance value computed between each landmark observation vector and each observation vector of the projected observation vectors. Nearest landmark observation vectors are selected from the landmark observation vectors for each observation vector. A second distance vector that includes a second distance value computed between each observation vector and each landmark observation vector is added to a feature distance matrix, where the second distance value is zero for each landmark observation vector not included in the nearest landmark observation vectors.

Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Abstract: A computing device determines a sparse feature representation for a machine learning model. Landmark observation vectors are randomly selected. Neighbor observation vectors are randomly selected that are less than a predefined distance from a selected landmark observation vector. The observation vectors are projected into a neighborhood subspace defined by principal components computed for the neighbor observation vectors. A distance vector includes a distance value computed between each landmark observation vector and each observation vector of the projected observation vectors. Nearest landmark observation vectors are selected from the landmark observation vectors for each observation vector. A second distance vector that includes a second distance value computed between each observation vector and each landmark observation vector is added to a feature distance matrix, where the second distance value is zero for each landmark observation vector not included in the nearest landmark observation vectors.

Abstract: A computing device provides a cluster connectivity graph presented on a display to summarize machine learning model performance. A classification value is predicted is predicted for a response variable value of each observation vector using a trained model. Observation vectors are divided into overlapping data slices that are separately clustered using the predicted classification value to define a set of clusters. A number of observations in each cluster is computed. An accuracy measure is computed for each cluster based on the predicted classification value. A number of overlapping observations between each pair of clusters is computed. The cluster connectivity graph includes a node for each cluster. A size of each node is determined from the computed number of observations. A fill-pattern of each node is determined from the computed accuracy measure. A connector line between each pair of nodes is determined from the computed number of overlapping observations.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: A computing device provides a cluster connectivity graph presented on a display to summarize machine learning model performance. A classification value is predicted is predicted for a response variable value of each observation vector using a trained model. Observation vectors are divided into overlapping data slices that are separately clustered using the predicted classification value to define a set of clusters. A number of observations in each cluster is computed. An accuracy measure is computed for each cluster based on the predicted classification value. A number of overlapping observations between each pair of clusters is computed. The cluster connectivity graph includes a node for each cluster. A size of each node is determined from the computed number of observations. A fill-pattern of each node is determined from the computed accuracy measure. A connector line between each pair of nodes is determined from the computed number of overlapping observations.

Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: A computing device to compute clusters using random subsets of variables is provided. Each data point of a plurality of data points is associated with a variable to define a plurality of variables. A subset of the plurality of variables is randomly selected. The subset does not include all of the plurality of variables. A number of clusters into which to segment the received data is determined. Cluster data that defines each cluster of the determined number of clusters is determined by executing a clustering algorithm with the received data using only the plurality of data points defined for each observation that are associated with the randomly selected subset of the plurality of variables. The determined cluster data is stored to cluster second data into the determined number of clusters. The second data is different from the received data.

Abstract: A computing device to select decorrelated variables using a graph based method is provided. A correlation value is computed between each pair of a plurality of variables to define a correlation matrix. A binary threshold value is compared to each correlation value to define a binary similarity matrix from the correlation matrix. An undirected graph comprising a subgraph that includes one or more connected nodes is defined based on the binary similarity matrix to store connectivity information for the plurality of variables. Each node of the subgraph is pairwise associated with a unique variable of the variables. (a) A least connected node is selected from the undirected graph based on the connectivity information. (b) The selected least connected node is removed from the undirected graph. (c) The connectivity information for the undirected graph is updated based on the removed node. (d) (a)-(c) are repeated until a stop criterion is satisfied.

Abstract: A computing device to compute composite clusters is provided. A first and a second plurality of centroid locations are computed by executing a clustering algorithm with a first portion of data and a first input parameter and a second portion of the data and a second input parameter, respectively. The first portion is different from the second portion or the first input parameter is different from the second input parameter. A plurality of composite centroid locations is computed using the computed first and second plurality of centroid locations to define a composite set of clusters. An observation is selected. A cluster of the composite set of clusters to which to assign the observation is determined using the plurality of composite centroid locations. The selecting and the determining is repeated with each observation of the plurality of observations as the observation to define cluster assignments for the plurality of observations.

Abstract: A method of determining a number of clusters for a dataset is provided. Centroid locations for a defined number of clusters are determined using a clustering algorithm. Boundaries for each of the defined clusters are defined. A reference distribution that includes a plurality of data points is created. The plurality of data points are within the defined boundary of at least one cluster of the defined clusters. Second centroid locations for the defined number of clusters are determined using the clustering algorithm and the reference distribution. A gap statistic for the defined number of clusters based on a comparison between a first residual sum of squares and a second residual sum of squares is computed. The processing is repeated for a next number of clusters to create. An estimated best number of clusters for the received data is determined by comparing the gap statistic computed for each iteration of the number of clusters.