Support Regularized Sparse Coding and Its Fast Encoder
Sparse coding represents a signal by a linear combination of only a few atoms of a learned over-complete dictionary. While sparse coding exhibits compelling performance for various machine learning tasks, the process of obtaining sparse code with fixed dictionary is independent for each data point without considering the geometric information and manifold structure of the entire data. We propose Support Regularized Sparse Coding (SRSC) which produces sparse codes that account for the manifold structure of the data by encouraging nearby data in the manifold to choose similar dictionary atoms. In this way, the obtained support regularized sparse codes capture the locally linear structure of the data manifold and enjoy robustness to data noise. We present the optimization algorithm of SRSC with theoretical guarantee for the optimization over the sparse codes. We also propose a feed-forward neural network termed Deep Support Regularized Sparse Coding (Deep-SRSC) as a fast encoder to approximate the sparse codes generated by SRSC. Extensive experimental results demonstrate the effectiveness of SRSC and Deep-SRSC.
The work proposes to use the geometry of data (that is considered to be known a priori) in order to have more consistent sparse coding. Namely, two data samples that are similar or neighbours, should have a sparse code that is similar (in terms of support). The general idea is not unique, but it is an interesting one (if one admits that the adjacency matrix A is known a priori), and the novelty mostly lies on the definition of the regularisation term that is an l1-norm (while other techniques would mostly use l2 regularisation). Based on this idea, the authors develop a new SRSC algorithm, which is analysed in detail and shown to perform better than its competitors based on l2 sparse coding regularisation and other schemes in terms of clustering performance. Inspired by LISTA, the authors then propose an approximate solution to the SRSC problem, called Deep-SRSC, that acts as a sort of fast encoder. Here too, the idea is interesting and seems to be quite efficient from experiments on USPS data, even if the framework seems to be strongly inspired from LISTA. That scheme should however be better motivated, by the limitations of SRSC that should be presented more clearly. Overall, the paper is well written, and pretty complete. It is not extremely original in its main ideas though, but the actual algorithm and implementation seem new and effective.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Support Regularized Sparse Coding and Its Fast Encoder
Sparse coding represents a signal by a linear combination of only a few atoms of a learned over-complete dictionary. While sparse coding exhibits compelling performance for various machine learning tasks, the process of obtaining sparse code with fixed dictionary is independent for each data point without considering the geometric information and manifold structure of the entire data. We propose Support Regularized Sparse Coding (SRSC) which produces sparse codes that account for the manifold structure of the data by encouraging nearby data in the manifold to choose similar dictionary atoms. In this way, the obtained support regularized sparse codes capture the locally linear structure of the data manifold and enjoy robustness to data noise. We present the optimization algorithm of SRSC with theoretical guarantee for the optimization over the sparse codes. We also propose a feed-forward neural network termed Deep Support Regularized Sparse Coding (Deep-SRSC) as a fast encoder to approximate the sparse codes generated by SRSC. Extensive experimental results demonstrate the effectiveness of SRSC and Deep-SRSC.
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 work proposes to use the geometry of data (that is considered to be known a priori) in order to have more consistent sparse coding. Namely, two data samples that are similar or neighbours, should have a sparse code that is similar (in terms of support). The general idea is not unique, but it is an interesting one (if one admits that the adjacency matrix A is known a priori), and the novelty mostly lies on the definition of the regularisation term that is an l1-norm (while other techniques would mostly use l2 regularisation). Based on this idea, the authors develop a new SRSC algorithm, which is analysed in detail and shown to perform better than its competitors based on l2 sparse coding regularisation and other schemes in terms of clustering performance. Inspired by LISTA, the authors then propose an approximate solution to the SRSC problem, called Deep-SRSC, that acts as a sort of fast encoder. Here too, the idea is interesting and seems to be quite efficient from experiments on USPS data, even if the framework seems to be strongly inspired from LISTA. That scheme should however be better motivated, by the limitations of SRSC that should be presented more clearly. Overall, the paper is well written, and pretty complete. It is not extremely original in its main ideas though, but the actual algorithm and implementation seem new and effective.
assistant
The work proposes to use the geometry of data (that is considered to be known a priori) in order to have more consistent sparse coding. Namely, two data samples that are similar or neighbours, should have a sparse code that is similar (in terms of support). The general idea is not unique, but it is an interesting one (if one admits that the adjacency matrix A is known a priori), and the novelty mostly lies on the definition of the regularisation term that is an l1-norm (while other techniques would mostly use l2 regularisation). Based on this idea, the authors develop a new SRSC algorithm, which is analysed in detail and shown to perform better than its competitors based on l2 sparse coding regularisation and other schemes in terms of clustering performance. Inspired by LISTA, the authors then propose an approximate solution to the SRSC problem, called Deep-SRSC, that acts as a sort of fast encoder. Here too, the idea is interesting and seems to be quite efficient from experiments on USPS data, even if the framework seems to be strongly inspired from LISTA. That scheme should however be better motivated, by the limitations of SRSC that should be presented more clearly. Overall, the paper is well written, and pretty complete. It is not extremely original in its main ideas though, but the actual algorithm and implementation seem new and effective.