TS-DIST: Learning Adaptive Distance Metric in Time Series Sets
AssigneeNEC Laboratories America, Inc.
InventorsTan Yan, Haifeng Chen, Guofei Jiang
Life of this patent
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Abstract
A process to control a machine by receiving data captured from one or more sensors in the machine generating high-dimensional time series sets in a machine; performing structure precomputing to obtain structures of different sets and time series in each set; performing supervised distance learning by imposing label information to the obtained structures, learning a transformation matrix; transforming the data to shrink a distance between sets with the same label and to stretch the distance between sets with different labels; and applying the transformed data to control the machine responsive to the time series data.
Claims20
A process to control a machine, comprising:
- receiving data captured from one or more sensors in the machine generating high-dimensional time series sets in a machine;
- performing structure precomputing to obtain structures of different sets and time series in each set;
- performing supervised distance learning by imposing label information to the obtained structures, learning a transformation matrix;
- transforming the data to shrink a distance between sets with the same label and to stretch the distance between sets with different labels;
- and
- applying the transformed data to control the machine responsive to the time series data.
The process of claim 9, comprising solving the SDP to get an optimal solution.
A system, comprising:
- an actuator;
- one or more sensors generating high-dimensional time series sets;
- a processor executing code for:
- performing structure precomputing to obtain structures of different sets and time series in each set;
- performing supervised distance learning by imposing label information to the obtained structures, learning a transformation matrix;
- transforming the data to shrink a distance between sets with the same label and to stretch the distance between sets with different labels;
- and
- wherein the actuator is controlled by the processor for applying the transformed data to control the actuator responsive to the time series data.
Dependencies are inferred from the claim text.