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Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
Concept Factorization (CF) and its variants may produce inaccurate repre...
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Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "s...
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Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
We investigate the high-dimensional data clustering problem by proposing...
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SA-Net: A deep spectral analysis network for image clustering
Although supervised deep representation learning has attracted enormous ...
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Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
We propose a novel and unsupervised representation learning model, i.e.,...
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CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks
With the great success of networks, it witnesses the increasing demand f...
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A generative approach to unsupervised deep local learning
Most existing feature learning methods optimize inflexible handcrafted f...
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Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features. To improve the representation and clustering abilities, DSCF-Net explicitly considers discovering hidden deep semantic features, enhancing the robustness proper-ties of the deep factorization to noise and preserving the local man-ifold structures of deep features. Specifically, DSCF-Net seamlessly integrates the robust deep concept factorization, deep self-expressive representation and adaptive locality preserving feature learning into a unified framework. To discover hidden deep repre-sentations, DSCF-Net designs a hierarchical factorization architec-ture using multiple layers of linear transformations, where the hierarchical representation is performed by formulating the prob-lem as optimizing the basis concepts in each layer to improve the representation indirectly. DSCF-Net also improves the robustness by subspace recovery for sparse error correction firstly and then performs the deep factorization in the recovered visual subspace. To obtain locality-preserving representations, we also present an adaptive deep self-representative weighting strategy by using the coefficient matrix as the adaptive reconstruction weights to keep the locality of representations. Extensive comparison results with several other related models show that DSCF-Net delivers state-of-the-art performance on several public databases.
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