3 research outputs found
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
We investigate the high-dimensional data clustering problem by proposing a
novel and unsupervised representation learning model called Robust Flexible
Auto-weighted Local-coordinate Concept Factorization (RFA-LCF). RFA-LCF
integrates the robust flexible CF, robust sparse local-coordinate coding and
the adaptive reconstruction weighting learning into a unified model. The
adaptive weighting is driven by including the joint manifold preserving
constraints on the recovered clean data, basis concepts and new representation.
Specifically, our RFA-LCF uses a L2,1-norm based flexible residue to encode the
mismatch between clean data and its reconstruction, and also applies the robust
adaptive sparse local-coordinate coding to represent the data using a few
nearby basis concepts, which can make the factorization more accurate and
robust to noise. The robust flexible factorization is also performed in the
recovered clean data space for enhancing representations. RFA-LCF also
considers preserving the local manifold structures of clean data space, basis
concept space and the new coordinate space jointly in an adaptive manner way.
Extensive comparisons show that RFA-LCF can deliver enhanced clustering
results.Comment: Accepted at the 44th IEEE International Conference on Acoustics,
Speech, and Signal Processing(ICASSP 2019
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the
"shallow" parts-based representation, but it clearly fails to discover deep
hierarchical information within both the basis and representation spaces. In
this paper, we technically propose a new enriched prior based Dual-constrained
Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net, for
learning the hierarchical coupled representations. To ex-tract hidden deep
features, DS2CF-Net is modeled as a deep-structure and geometrical
structure-constrained neural network. Specifically, DS2CF-Net designs a deep
coupled factorization architecture using multi-layers of linear
transformations, which coupled updates the bases and new representations in
each layer. To improve the discriminating ability of learned deep
representations and deep coefficients, our network clearly considers enriching
the supervised prior by the joint deep coefficients-regularized label
prediction, and incorporates enriched prior information as additional label and
structure constraints. The label constraint can enable the samples of the same
label to have the same coordinate in the new feature space, while the structure
constraint forces the coefficient matrices in each layer to be block-diagonal
so that the enhanced prior using the self-expressive label propagation are more
accurate. Our network also integrates the adaptive dual-graph learning to
retain the local manifold structures of both the data manifold and feature
manifold by minimizing the reconstruction errors in each layer. Extensive
experiments on several real databases demonstrate that our DS2CF-Net can obtain
state-of-the-art performance for representation learning and clustering
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
Concept Factorization (CF) and its variants may produce inaccurate
representation and clustering results due to the sensitivity to noise, hard
constraint on the reconstruction error and pre-obtained approximate
similarities. To improve the representation ability, a novel unsupervised
Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF)
framework is proposed for clustering high-dimensional data. Specifically,
RFA-LCF integrates the robust flexible CF by clean data space recovery, robust
sparse local-coordinate coding and adaptive weighting into a unified model.
RFA-LCF improves the representations by enhancing the robustness of CF to noise
and errors, providing a flexible constraint on the reconstruction error and
optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a
sparse projection to recover the underlying clean data space, and then the
flexible CF is performed in the projected feature space. RFA-LCF also uses a
L2,1-norm based flexible residue to encode the mismatch between the recovered
data and its reconstruction, and uses the robust sparse local-coordinate coding
to represent data using a few nearby basis concepts. For auto-weighting,
RFA-LCF jointly preserves the manifold structures in the basis concept space
and new coordinate space in an adaptive manner by minimizing the reconstruction
errors on clean data, anchor points and coordinates. By updating the
local-coordinate preserving data, basis concepts and new coordinates
alternately, the representation abilities can be potentially improved.
Extensive results on public databases show that RFA-LCF delivers the
state-of-the-art clustering results compared with other related methods.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (IEEE
TKDE