2 research outputs found
Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering
Deep multi-view clustering methods have achieved remarkable performance.
However, all of them failed to consider the difficulty labels (uncertainty of
ground-truth for training samples) over multi-view samples, which may result
into a nonideal clustering network for getting stuck into poor local optima
during training process; worse still, the difficulty labels from multi-view
samples are always inconsistent, such fact makes it even more challenging to
handle. In this paper, we propose a novel Deep Adversarial Inconsistent
Cognitive Sampling (DAICS) method for multi-view progressive subspace
clustering. A multiview binary classification (easy or difficult) loss and a
feature similarity loss are proposed to jointly learn a binary classifier and a
deep consistent feature embedding network, throughout an adversarial minimax
game over difficulty labels of multiview consistent samples. We develop a
multi-view cognitive sampling strategy to select the input samples from easy to
difficult for multi-view clustering network training. However, the
distributions of easy and difficult samples are mixed together, hence not
trivial to achieve the goal. To resolve it, we define a sampling probability
with theoretical guarantee. Based on that, a golden section mechanism is
further designed to generate a sample set boundary to progressively select the
samples with varied difficulty labels via a gate unit, which is utilized to
jointly learn a multi-view common progressive subspace and clustering network
for more efficient clustering. Experimental results on four real-world datasets
demonstrate the superiority of DAICS over the state-of-the-art methods
A Critique of Self-Expressive Deep Subspace Clustering
Subspace clustering is an unsupervised clustering technique designed to
cluster data that is supported on a union of linear subspaces, with each
subspace defining a cluster with dimension lower than the ambient space. Many
existing formulations for this problem are based on exploiting the
self-expressive property of linear subspaces, where any point within a subspace
can be represented as linear combination of other points within the subspace.
To extend this approach to data supported on a union of non-linear manifolds,
numerous studies have proposed learning an embedding of the original data using
a neural network which is regularized by a self-expressive loss function on the
data in the embedded space to encourage a union of linear subspaces prior on
the data in the embedded space. Here we show that there are a number of
potential flaws with this approach which have not been adequately addressed in
prior work. In particular, we show the model formulation is often ill-posed in
that it can lead to a degenerate embedding of the data, which need not
correspond to a union of subspaces at all and is poorly suited for clustering.
We validate our theoretical results experimentally and also repeat prior
experiments reported in the literature, where we conclude that a significant
portion of the previously claimed performance benefits can be attributed to an
ad-hoc post processing step rather than the deep subspace clustering model.Comment: Published as a conference paper at the International Conference on
Learning Representations (ICLR) 202