Shuli's Master's thesis defense on Tuesday (April.21)

[Shuli Jiang]

Dear Autonians,

I will be holding my Master's thesis defense next *Tuesday (April.21)
16:30pm ~ 17:30pm*, on "Deep Multi-view Clustering Using Local Similarity
Graphs".

You are welcome to attend if you are interested.

Zoom link: https://cmu.zoom.us/j/99312100561

Thesis committee members: Prof. Artur Dubrawski (Chair), Prof. Jeff
Schneider

Title: Deep Multi-view Clustering Using Local Similarity Graphs

Abstract:

Multi-view clustering involves clustering data with different, possibly
distinct feature sets simultaneously. In many application domains,
multi-view data arises naturally. For example, news can be described by
both text and pictures, and multimedia segments can be described by their
video signals from cameras and audio signals from voice recorders.
Multi-view clustering has a wide range of potentially impactful
applications. Yet, the benefits of using graph-based local similarity
information to learn better representations of data for clustering, and the
flexibility of incorporating pairwise constraints which may be accessible
to improve clustering performance, are still under-explored in multi-view
clustering.

We present Local Similarity Graph based Multi-view Clustering (LSGMC), a
new and improved correlation based multi-view clustering approach. The
method leverages local similarity graphs constructed by mutual K nearest
neighbors. LSGMC uses the graphs to guide search for a better data
representation through exploring first order proximity within views, and
utilizing complementary information across views. We empirically show that
LSGMC can efficiently use information from multiple views to improve
clustering accuracy, and outperform state-of-the-art multi-view
alternatives on a variety of benchmark and real world datasets, including
image data for hand digit recognition, text data for language recognition
and acoustic-articulatory data for speech recognition. We further show that
LSGMC is flexible in incorporating pairwise constraints and thus it can be
naturally extended to handle semi-supervised learning problems.

Thesis document:
http://www.andrew.cmu.edu/user/shulij/master_thesis.pdf


Happy Friday.

Cheers,
Shuli

-- 
*Shuli Jiang*
Carnegie Mellon University
B.S. Computer Science, 2019
M.S. Computer Science, 2020
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