From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper29/AnonReviewer1
evaluation: This is an interesting extension to MSSA along the line of cPCA and quite relevant to this workshop.

From: ~Ransalu_Senanayake1
comment: This paper proposes unsupervised machine learning algorithm for dimensionality reduction and signal decomposition of time-series data. In contrast to the common practice of considering only the subsignals that explain the highest variance such as in MSSA and PCA, the proposed technique highlights the signals that matter. Experiments are conducted on both toy datasets and ECG data. The paper is supplemented with extra material.

Authors should consider adding a motivating figure to explain the concept "In situations where the information of interest explains little overall variance, these methods fail to reveal it. Recently, extensions like contrastive PCA (cPCA) have shown that utilizing a background dataset...
can help better discover structure...in the foreground (target) that is of interest to the analyst."

Fig 1 is a great summary. Please check the title-"Shematic".  It would be useful to have a discussion on the importance of Hankel matrices for dimensionality reduction. The contrastive MSSA introduces a new parameter \alpha and the paper proposes several promising values for it. The paper should add an in-depth discussion on the broad literature on dimensionality reduction and spectrum analysis. Especially, there are inherent relationships between the Hankel matrix and SVD. 

    1. What did you learn from reading this paper that you did not know before?
cPCA
    2. What was the most exciting part of the work?
Considering "signals that matter" to the analyst 
    3. What in your opinion makes this work stand apart from other papers in this area?
Considering "signals that matter" to the analyst 
    4. Does the paper fit into the scope of the workshop?
In to a certain extent. It is a multivariate time series problem. It can be considered as a spatiotemporal problem because of the ECG examples. 
    5. Are you aware of relevant literature that you may want to make the authors aware of?
No.
title: From: NIPS 2018 Workshop Spatiotemporal Paper29 AnonReviewer3

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper29/AnonReviewer1
relevance: 4: high
impact: 3: borderline
novelty: 3: borderline