Proceedings ArticleDOI
Latent Space Sparse Subspace Clustering
Vishal M. Patel,Hien M. Nguyen,René Vidal +2 more
- pp 225-232
TLDR
A method that learns the projection of data and finds the sparse coefficients in the low-dimensional latent space and applies spectral clustering to a similarity matrix built from these sparse coefficients.Abstract:
We propose a novel algorithm called Latent Space Sparse Subspace Clustering for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe a method that learns the projection of data and finds the sparse coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these sparse coefficients. An efficient optimization method is proposed and its non-linear extensions based on the kernel methods are presented. One of the main advantages of our method is that it is computationally efficient as the sparse coefficients are found in the low-dimensional latent space. Various experiments show that the proposed method performs better than the competitive state-of-the-art subspace clustering methods.read more
Citations
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Generalized Latent Multi-View Subspace Clustering
TL;DR: This work proposes a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC), which explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation.
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Latent Multi-view Subspace Clustering
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Structured AutoEncoders for Subspace Clustering.
TL;DR: This work proposes a novel subspace clustering approach by introducing a new deep model—Structured AutoEncoder (StructAE), which learns a set of explicit transformations to progressively map input data points into nonlinear latent spaces while preserving the local and global subspace structure.
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Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
TL;DR: In this paper, a joint dimensionality reduction and k-means clustering approach is proposed, in which the deep neural network (DNN) is employed to jointly optimize the two tasks, while exploiting the DNN's ability to approximate any nonlinear function.
References
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GradientBased Learning Applied to Document Recognition
Simon Haykin,Bart Kosko +1 more
TL;DR: Various methods applied to handwritten character recognition are reviewed and compared and Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.