Sparse coding of hidden states for explanatory purposes
First Claim
1. A method comprising:
- maintaining, by a device in a network, a machine learning-based recursive model that models a time series of observations regarding a monitored entity in the network;
applying, by the device, sparse dictionary learning to the recursive model, to find a decomposition of a particular state vector of the recursive model, wherein the decomposition of the particular state vector comprises a plurality of basis vectors;
determining, by the device, a mapping between at least one of the plurality of basis vectors for the particular state vector and one or more human-readable interpretations of the basis vectors;
providing, by the device, a label for the particular state vector to a user interface, wherein the label is based on the mapping between the at least one of the plurality of basis vectors for the particular state vector and the one or more human-readable interpretations of the basis vectors;
determining, by the device, that a particular one of the basis vectors does not have a human-readable interpretation based on the mapping; and
ignoring, by the device, the particular basis vector when generating the label for the state vector.
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Abstract
In one embodiment, a device in a network maintains a machine learning-based recursive model that models a time series of observations regarding a monitored entity in the network. The device applies sparse dictionary learning to the recursive model, to find a decomposition of a particular state vector of the recursive model. The decomposition of the particular state vector comprises a plurality of basis vectors. The device determines a mapping between at least one of the plurality of basis vectors for the particular state vector and one or more human-readable interpretations of the basis vectors. The device provides a label for the particular state vector to a user interface. The label is based on the mapping between the at least one of the plurality of basis vectors for the particular state vector and the one or more human-readable interpretations of the basis vectors.
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Citations
18 Claims
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1. A method comprising:
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maintaining, by a device in a network, a machine learning-based recursive model that models a time series of observations regarding a monitored entity in the network; applying, by the device, sparse dictionary learning to the recursive model, to find a decomposition of a particular state vector of the recursive model, wherein the decomposition of the particular state vector comprises a plurality of basis vectors; determining, by the device, a mapping between at least one of the plurality of basis vectors for the particular state vector and one or more human-readable interpretations of the basis vectors; providing, by the device, a label for the particular state vector to a user interface, wherein the label is based on the mapping between the at least one of the plurality of basis vectors for the particular state vector and the one or more human-readable interpretations of the basis vectors; determining, by the device, that a particular one of the basis vectors does not have a human-readable interpretation based on the mapping; and ignoring, by the device, the particular basis vector when generating the label for the state vector. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An apparatus, comprising:
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one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed operable to; maintain a machine learning-based recursive model that models a time series of observations regarding a monitored entity in the network; apply sparse dictionary learning to the recursive model, to find a decomposition of a particular state vector of the recursive model, wherein the decomposition of the particular state vector comprises a plurality of basis vectors; determine a mapping between at least one of the plurality of basis vectors for the particular state vector and one or more human-readable interpretations of the basis vectors; and provide a label for the particular state vector to a user interface, wherein the label is based on the mapping between the at least one of the plurality of basis vectors for the particular state vector and the one or more human-readable interpretations of the basis vectors. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device in a network to execute a process comprising:
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maintaining, by the device, a machine learning-based recursive model that models a time series of observations regarding a monitored entity in the network; applying, by the device, sparse dictionary learning to the recursive model, to find a decomposition of a particular state vector of the recursive model, wherein the decomposition of the particular state vector comprises a plurality of basis vectors; determining, by the device, a mapping between at least one of the plurality of basis vectors for the particular state vector and one or more human-readable interpretations of the basis vectors; providing, by the device, a label for the particular state vector to a user interface, wherein the label is based on the mapping between the at least one of the plurality of basis vectors for the particular state vector and the one or more human-readable interpretations of the basis vectors; determining, by the device, that a particular one of the basis vectors does not have a human-readable interpretation based on the mapping; and ignoring, by the device, the particular basis vector when generating the label for the state vector. - View Dependent Claims (18)
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Specification