Unsupervised classification into unknown number of classes
We propose a novel unsupervised classification method based on graph Laplacian. Unlike the widely used classification method, this architecture does not require the labels of data and the number of classes. Our key idea is to introduce a approximate linear map and a spectral clustering theory on the dimension reduced spaces into generative adversarial networks. Inspired by the human visual recognition system, the proposed framework can classify and also generate images as the human brains do. We build an approximate linear connector network analogous to the cerebral cortex, between the discriminator and the generator . The connector network allows us to estimate the unknown number of classes. Estimating the number of classes is one of the challenging researches in the unsupervised learning, especially in spectral clustering. The proposed method can also classify the images by using the estimated number of classes. Therefore, we define our method as an unsupervised classification method.
 The manuscript proposes a method for unsupervised learning with unknown class number k. The problem is classical and important. The proposed method is interesting and novel, but the experiments are not convincing. In detail, it did not compare other methods in the experiments. Pros: clear description and novelty of the method Cons: insufficient experiments.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unsupervised classification into unknown number of classes
We propose a novel unsupervised classification method based on graph Laplacian. Unlike the widely used classification method, this architecture does not require the labels of data and the number of classes. Our key idea is to introduce a approximate linear map and a spectral clustering theory on the dimension reduced spaces into generative adversarial networks. Inspired by the human visual recognition system, the proposed framework can classify and also generate images as the human brains do. We build an approximate linear connector network analogous to the cerebral cortex, between the discriminator and the generator . The connector network allows us to estimate the unknown number of classes. Estimating the number of classes is one of the challenging researches in the unsupervised learning, especially in spectral clustering. The proposed method can also classify the images by using the estimated number of classes. Therefore, we define our method as an unsupervised classification method.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The manuscript proposes a method for unsupervised learning with unknown class number k. The problem is classical and important. The proposed method is interesting and novel, but the experiments are not convincing. In detail, it did not compare other methods in the experiments. Pros: clear description and novelty of the method Cons: insufficient experiments.
assistant
 The manuscript proposes a method for unsupervised learning with unknown class number k. The problem is classical and important. The proposed method is interesting and novel, but the experiments are not convincing. In detail, it did not compare other methods in the experiments. Pros: clear description and novelty of the method Cons: insufficient experiments.