Learning Low-Rank Embeddings Using Hough Forest and Hough Factorized Low-Rank Pooling


Learning Low-Rank Embeddings Using Hough Forest and Hough Factorized Low-Rank Pooling – This work is designed to generalize the proposed algorithm to datasets with linear or nonlinear dimensions. It first estimates Hough coefficients and then constructs discriminative representations of the data by a single classifier. The data is estimated by using two classes of learning functions: linear and nonlinear. The discriminative representations are represented using the linear model as a latent variable vector, which is a nonparametric representation of high-dimensional data. Given the discriminative representations, a second classifier is chosen to predict the data distribution. The discriminative representations are then combined for the joint classification problem. The proposed algorithm is implemented using a distributed framework and is evaluated on the MNIST dataset with a wide class of data and a large number of labeled images. Experimental results on both MNIST and CIFAR-10 datasets demonstrate that a combination of learning with discriminative representations is beneficial for both classification and segmentation applications.

The main contributions of this study are two-fold. First, we propose a novel framework for multi-attribute classification of high-dimensional vectors with several attributes, where the number of attributes is fixed in the model parameters. Second, we propose to use a novel loss function to reduce the dimensionality of these models. This loss is derived by maximizing the Euclidean distance between the two attribute vectors which can reduce the number of model parameters. To improve training, the proposed model is evaluated to predict the predicted labels and the predicted attributes. Results on synthetic data and real datasets demonstrate that our approach outperforms the state-of-the-art multi- attribute classification methods.

Bayesian Inference via Adversarial Decompositions

Fast Spatial-Aware Image Interpretation

Learning Low-Rank Embeddings Using Hough Forest and Hough Factorized Low-Rank Pooling

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  • An Ensemble-based Benchmark for Named Entity Recognition and Verification

    Learning Multi-Attribute Classification Models for Semi-Supervised ClassificationThe main contributions of this study are two-fold. First, we propose a novel framework for multi-attribute classification of high-dimensional vectors with several attributes, where the number of attributes is fixed in the model parameters. Second, we propose to use a novel loss function to reduce the dimensionality of these models. This loss is derived by maximizing the Euclidean distance between the two attribute vectors which can reduce the number of model parameters. To improve training, the proposed model is evaluated to predict the predicted labels and the predicted attributes. Results on synthetic data and real datasets demonstrate that our approach outperforms the state-of-the-art multi- attribute classification methods.


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