Deep Multi-View Learning via Task-Optimal CCA
Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels. Recent CCA methods have started to address this weakness but are limited in that they do not simultaneously optimize the CCA projection for discrimination and the CCA projection itself, or they are linear only. We address these deficiencies by simultaneously optimizing a CCA-based and a task objective in an end-to-end manner. Together, these two objectives learn a non-linear CCA projection to a shared latent space that is highly correlated and discriminative. Our method shows a significant improvement over previous state-of-the-art (including deep supervised approaches) for cross-view classification (8.5% increase), regularization with a second view during training when only one view is available at test time (2.2-3.2%), and semi-supervised learning (15%) on real data.
This is a interesting paper on an important topic, but it was a main weakness: it assumes that the reader is deeply familiar with the CCA. In order to make the paper more accessible to a general audience, the authors should: 1) have at least one sentence in the abstract that explains in layman terms why is CCA important and how it works (*multi-view learning* does not suffice); given that you have the term *multi-view learning* in the title, you should explain what it is and how it can benefit from CCA 2) re-organize the current intro, which reads more like related work, into a more traditional format - one intuitive paragraph on what is multi-view learning (MVL), what is CCA, how does CCA help MVL - one intuitive paragraph on an illustrative example on how MVL & CCA help solving a problem - one intuitive paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 3) ideally, add a section with an illustrative running example, which would have a huge impact on the paper*s readability (far more than, say, than the current Appendix)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Multi-View Learning via Task-Optimal CCA
Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels. Recent CCA methods have started to address this weakness but are limited in that they do not simultaneously optimize the CCA projection for discrimination and the CCA projection itself, or they are linear only. We address these deficiencies by simultaneously optimizing a CCA-based and a task objective in an end-to-end manner. Together, these two objectives learn a non-linear CCA projection to a shared latent space that is highly correlated and discriminative. Our method shows a significant improvement over previous state-of-the-art (including deep supervised approaches) for cross-view classification (8.5% increase), regularization with a second view during training when only one view is available at test time (2.2-3.2%), and semi-supervised learning (15%) on real data.
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>



This is a interesting paper on an important topic, but it was a main weakness: it assumes that the reader is deeply familiar with the CCA. In order to make the paper more accessible to a general audience, the authors should: 1) have at least one sentence in the abstract that explains in layman terms why is CCA important and how it works (*multi-view learning* does not suffice); given that you have the term *multi-view learning* in the title, you should explain what it is and how it can benefit from CCA 2) re-organize the current intro, which reads more like related work, into a more traditional format - one intuitive paragraph on what is multi-view learning (MVL), what is CCA, how does CCA help MVL - one intuitive paragraph on an illustrative example on how MVL & CCA help solving a problem - one intuitive paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 3) ideally, add a section with an illustrative running example, which would have a huge impact on the paper*s readability (far more than, say, than the current Appendix)
assistant
This is a interesting paper on an important topic, but it was a main weakness: it assumes that the reader is deeply familiar with the CCA. In order to make the paper more accessible to a general audience, the authors should: 1) have at least one sentence in the abstract that explains in layman terms why is CCA important and how it works (*multi-view learning* does not suffice); given that you have the term *multi-view learning* in the title, you should explain what it is and how it can benefit from CCA 2) re-organize the current intro, which reads more like related work, into a more traditional format - one intuitive paragraph on what is multi-view learning (MVL), what is CCA, how does CCA help MVL - one intuitive paragraph on an illustrative example on how MVL & CCA help solving a problem - one intuitive paragraph on how the proposed approach works - one paragraph summarizing the main findings/results 3) ideally, add a section with an illustrative running example, which would have a huge impact on the paper*s readability (far more than, say, than the current Appendix)