Robust Sparse Subspace Clustering

Robust Sparse Subspace Clustering – We propose a novel technique and computational framework for unsupervised clustering of low-level features from a large unlabeled collection of labeled data (e.g., image data) with minimal training set. In order to obtain the best of both worlds, we propose to exploit low-level features and clusters in a highly discriminative manner, which is the best case scenario for our approach. We first apply the method to image data and demonstrate its usefulness for unsupervised clustering based on the similarity between the clustering result and the labels. We further use it to analyze the effect of labels and then evaluate whether or not a high-level clustering method can have the same performance. Experimental results show that using low-level features, which is desirable for image data, outperforms the supervised clustering approach, as well as other clustering methods proposed in this paper.

This paper presents a method to find the optimal distribution of the maximum local minimum with the goal to learn the right distribution based on the input and the information from the source. Our key idea is to learn the distribution of the maximum local min of the input vector in terms of the local minimum, and infer a set of local min distributions corresponding to this distribution. We show that this distribution can be easily achieved even when the input is very sparse in Gaussian. Therefore, the learning rate and the inference time can scale linearly with the number of input vectors. Furthermore, the estimation error can be controlled with stochastic nonstationary regularization, which shows that this nonstationary regularization can be achieved only when the input is very sparse. Our experimental results show that on several real datasets this regularizer can be easily applied to almost any distribution.

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Robust Sparse Subspace Clustering

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    Stochastic Variational Autoencoder for Robust and Fast Variational Image-Level LearningThis paper presents a method to find the optimal distribution of the maximum local minimum with the goal to learn the right distribution based on the input and the information from the source. Our key idea is to learn the distribution of the maximum local min of the input vector in terms of the local minimum, and infer a set of local min distributions corresponding to this distribution. We show that this distribution can be easily achieved even when the input is very sparse in Gaussian. Therefore, the learning rate and the inference time can scale linearly with the number of input vectors. Furthermore, the estimation error can be controlled with stochastic nonstationary regularization, which shows that this nonstationary regularization can be achieved only when the input is very sparse. Our experimental results show that on several real datasets this regularizer can be easily applied to almost any distribution.


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