Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given a motion capture sequence, how to identify the category of the motion? Classifying human motions is a critical task in motion editing and synthesizing, for which manual labeling is clearly inecient for large databases. Here  study the general problem of time series clustering.  propose a novel method of clustering time series that can (a) learn joint temporal dynamics in the data; (b) handle time lags; and (c) produce interpretable features.  achieve this by developing complex-valued linear dynamical systems (CLDS), which include real-valued Kalman lters as a special case; our advantage is that the transition matrix is simpler (just diagonal), and the transmission one easier to interpret.  then present ComplexFit, a novel EM algorithm to learn the parameters for the general model and its special case for clustering. Our approach produces signicant improvement in clustering quality, 1.5 to 5 times better than ll-known competitors on real motion capture sequences.
['Lei Li', 'B. A. Prakash']
I am writing a research paper on a idea Time Series Clustering: Complex is Simpler!
[{"from": "human", "value": "I am writing a research paper on a idea Time Series Clustering: Complex is Simpler!"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given a motion capture sequence, how to identify the category of the motion? Classifying human motions is a critical task in motion editing and synthesizing, for which manual labeling is clearly inecient for large databases. Here  study the general problem of time series clustering.  propose a novel method of clustering time series that can (a) learn joint temporal dynamics in the data; (b) handle time lags; and (c) produce interpretable features.  achieve this by developing complex-valued linear dynamical systems (CLDS), which include real-valued Kalman lters as a special case; our advantage is that the transition matrix is simpler (just diagonal), and the transmission one easier to interpret.  then present ComplexFit, a novel EM algorithm to learn the parameters for the general model and its special case for clustering. Our approach produces signicant improvement in clustering quality, 1.5 to 5 times better than ll-known competitors on real motion capture sequences."}]