Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Clustering and Multi-Manifold Learning with Geometric
  Structure Preservation   Though manifold-based clustering has become a popular research topic, we
observe that one important factor has been omitted by these works, namely that
the defined clustering loss may corrupt the local and global structure of the
latent space. In this paper, we propose a novel Generalized Clustering and
Multi-manifold Learning (GCML) framework with geometric structure preservation
for generalized data, i.e., not limited to 2-D image data and has a wide range
of applications in speech, text, and biology domains. In the proposed
framework, manifold clustering is done in the latent space guided by a
clustering loss. To overcome the problem that the clustering-oriented loss may
deteriorate the geometric structure of the latent space, an isometric loss is
proposed for preserving intra-manifold structure locally and a ranking loss for
inter-manifold structure globally. Extensive experimental results have shown
that GCML exhibits superior performance to counterparts in terms of qualitative
visualizations and quantitative metrics, which demonstrates the effectiveness
of preserving geometric structure.

11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int