Rodney Sparapini writes:

I got this in my inbox today. I thought this might be of interest to you and your blog readers.

It’s not at all of interest to me but it might interest some of my readers. I’m posting it here because there’s something amazing about seeing this intense dispute about something I’ve never heard of.

OK, here it is:

From: ELM Exposed [email protected]
Subject: The ELM Scandal: What You May Not Know about the Extreme
Learning Machines
Date: Tue, 4 Aug 2015 04:09:35 -0700

Dear Researcher,

The objective of launching this homepage
(http://elmorigin.wix.com/originofelm) is to present the evidences
regarding the tainted origins of the extreme learning machines (ELM). As
we would like all readers to verify the facts within a short period of
time (perhaps 10 to 20 minutes), we have uploaded a dozen of PDF files
with highlights and annotations clearly showing the following:

1. The kernel (or constrained-optimization-based) version of ELM
(ELM-Kernel, Huang 2012) is identical to kernel ridge regression (for
regression and single-output classification, Saunders ICML 1998, as well
as the LS-SVM with zero bias; for multiclass multi-output
classification, An CVPR 2007).

2. ELM-SLFN (the single-layer feedforward network version of the ELM,
Huang IJCNN 2004) is identical to the randomized neural network (RNN,
with omission of bias, Schmidt 1992) and another simultaneous work,
i.e., the random vector functional link (RVFL, with omission of direct
input-output links, Pao 1994).

3. ELM-RBF (Huang ICARCV 2004) is identical to the randomized RBF neural
network (Broomhead-Lowe 1988, with a performance-degrading
randomization of RBF radii or impact factors).

4. In all three cases above, Huang got his papers published after
excluding a large volume of very closely related literature.

5. Hence, all 3 “ELM variants” have absolutely no technical originality,
promote unethical research practices among researchers, and steal
citations from original inventors. For easy verifications on the origins
of the ELM, with annotated PDF files, please visit:

http://elmorigin.wix.com/originofelm

Please forward this message to your contacts so that others can also
study the materials presented at this website and take appropriate
actions, if necessary.

ELM: The Sociological Phenomenon

Since the invention of the name “extreme learning machines (ELM)” in
2004, the number of papers and citations on the ELM has been increasing
exponentially. How can this be imaginable for the ELM comprising of 3
decade-old algorithms published by authors other than the ELM inventor?
This phenomenon would not have been possible without the support and
participation of researchers on the fringes of machine learning. Some
(unknowingly and a few knowingly) love the ELM for various reasons:

• Some authors love the ELM, because it is always easy to
publish ELM papers in an ELM conference or an ELM special issue. For
example, one can simply take a decade-old paper on a variant of RVFL,
RBF or kernel ridge regression and re-publish it as a variant of the
ELM, after paying a small price of adding 10s of citations on Huang’s
“classic ELM papers”.

• A couple of editor-in-chiefs (EiCs) love the ELM and offer
multiple special issues/invited papers, because the ELM conference &
special issues will bring a flood of papers, many citations and
therefore high impact factors to their low quality journals. The EiCs
can claim to have faithfully worked within the peer-review system, i.e.
the ELM submissions are all rigorously reviewed by ELM experts.

• A few technical leaders, e.g. some IEEE society officers,
love the ELM, because it rejuvenates the community by bringing in more
activities and subscriptions.

• A couple of funding agencies love the ELM, because they
would rather fund a new sexy name, than any genuine research.

One may ask: how can something loved by so many be wrong?

A leading cause of the current Greek economic crisis was that a previous
government showered its constituents with jobs and lucrative
compensations, in order to gain their votes, thereby raising the debt to
an unsustainable level. At that time, the government behavior was
welcome by many, but led to severe consequences. Another example of
popularity leading to a massive disaster can be found in WW II as Hitler
was elected by popular votes.

The seemingly small price to pay in the case of the ELM is the
diminished publishing ethics, which, in a long run, will fill the
research literature with renamed junk, thereby making the research
community and respected names, such as IEEE, Thomson Reuters, Springer
and Elsevier, laughing stocks. Similar to that previous Greek government
and its supporting constituents, the ELM inventor and his supporters are
“borrowing” from the future of the entire research community for their
present enjoyment! It is time to wake up to your consciousness.

Our beloved peer-review system was grossly abused and failed
spectacularly in the case of the ELM. It is time for the machine
learning experts and leaders to investigate the allegations presented
hereand to take corrective actions soon.

5 Easy but Proven Steps to Fame

1. The Brink of Genius: Take a paper published about 20 years ago (so
that the original authors have either passed away, retired, or are too
well-established/generous to publicly object. Unfortunately, pioneers
like Broomhead and Pao have passed away). Introduce a very minor
variation, for example, by fixing one of the tunable parameters at zero
(who cares if this makes the old method worse, as long as you can claim
it is now different and faster). Rewrite the paper in such a way that
plagiarism software cannot detect the similarity, so that you are not in
any of the “IEEE 5 levels of plagiarism”. Give a completely new
sensational name (hint: the word “extreme” sounds extremely sexy).

2. Publication: Submit your paper(s) to a poor quality conference or
journal without citing any related previous works.

3. Salesmanship: After publishing such a paper, now it is time to sell
the stolen goods!Never blush. Don’t worry about ethics. Get your
friends/colleagues to use your “big thing”. Put up your Matlab program
for download. Organize journal special issues, conferences, etc. to
promote these unethical research practices among junior researchers who
would just trust your unethical publications without bothering to read
the original works published in the 1980s or 1990s. Of course, the
pre-requisite for a paper to be accepted in your special
issues/conferences is 10s of citations for your unethically created name
and publications. Invite big names to be associated with your
unethically created name as advisory board members, keynote speakers, or
co-authors. These people may be too busy to check the details (with a
default assumption that your research is ethical) and/or too nice to say
no. But, once “infected” with your unethically created name, they will
be obliged to defend it for you.

4. The Smoke Screen: Should others point out the original work, you
claim not to know the literature while pointing to a minor variation
that you introduced in the first place. Instead of accepting that your
work was almost the same as the literature and reverting back to the
older works, you promote your work by: (1) repeating the tiny variation;
(2) excluding the almost identical works in the list of references or
citing and describing them incorrectly; (3) excluding thorough
experimental comparisons with nearly identical works in the literature
so that worse performance of your minute variations will not be exposed;
(4) making negative statements about competing methods and positive
statements about your unethically created name without solid
experimental results using words like “may” or “analysis”; (5) comparing
with apparently different methods. You can copy the theories and proofs
derived for other methods and apply to your method (with tiny variation
from those in the old literature) claim that your method has got a lot
of theories while others do not have.

5. Fame: Declare yourself as a research leader so that junior
researchers can follow your footsteps. Enjoy your new fortune, i.e.,
high citations, invited speeches, etc. You don’t need to be on the
shoulders of giants, because you are a giant! All you have to do to get
there is to follow these easy steps!

One can call the above steps “IP” (Intelligent Plagiarism), as opposed
to stupid (verbatim) plagiarism specified by the IEEE in “5 levels”. The
machine learning community should feel embarrassed if “IP” (Intelligent
Plagiarism) was originally developed and/or grandiosely promoted by this
community, while the community is supposed to create other (more
ethical) intelligent algorithms to benefit the mankind.

In mid-July 2015, G.-B. Huang posted an email on his
[email protected] emailing list. This email was forwarded to
[email protected] for our responses. As usual, this email was
meaningless and our remarks are attached.

And also this pdf, which you can read if you’re not tired of this yet.

I just have a few comments about the above message:

1. Hitler never received much more than a third of the vote in a fair election.

2. I thought Elsevier was already a laughing stock?

3. I’d hardly call this a path to fame, given that I’d never heard of this Huang character.

4. There’s nothing wrong with putting up a Matlab program for download, right?

5. I’m kinda doubting that invited speeches will lead to fortune. Free flights, sure, but probably not much more than that.