METHOD AND SYSTEM FOR MULTI-LABEL PREDICTION
A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for multi-label prediction comprises generating a label space; receiving a data point from a user; generating a first feature vector from the data point; projecting the first feature vector to the label space; determining a first set of labels associated with the first feature vector from the label space; converting the first set of labels to a second set of labels; and providing the second set of labels to the user.
1 . A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for multi-label prediction, the method comprising:
generating a label space;
receiving a data point from a user;
generating a first feature vector from the data point;
projecting the first feature vector to the label space;
determining a first set of labels associated with the first feature vector from the label space;
converting the first set of labels to a second set of labels; and
providing the second set of labels to the user.
2 . The method of claim 1 , wherein generating the label space further comprises:
obtaining a plurality of data samples from at least a knowledge base;
generating a plurality of second feature vectors respectively associated with the plurality of data samples;
extracting one or more second labels associated with the plurality of second feature vectors;
generating a first label matrix based on the plurality of second feature vectors and the one or more second labels;
transforming the first label matrix to a second label matrix;
training one or more parameters associated with the second label matrix; and
generating the label space based on the second label matrix and the trained one or more parameters.
3 . The method of claim 2 , wherein each element of the first label matrix indicates a relation as to whether one of the plurality of second vectors is annotated by one of the one or more second labels.
4 . The method of claim 2 , wherein transforming the first label matrix to a second label matrix further comprises:
performing dimensionality reduction on the first label matrix based on random rejection, wherein a first dimension of the first label matrix representing a number of labels is reduced to a pre-determined value in the second label matrix.
5 . The method of claim 2 , wherein the one or more parameters associated with the second label matrix is trained by a least square regression model.
6 . The method of claim 2 , wherein the first feature vector is projected to the label space using the one or more parameters associated with the second label matrix.
7 . The method of claim 1 , wherein determining a first set of labels associated with the first feature vector from the label space further comprises:
selecting a pre-determined number of candidates from the label space using k-nearest neighbor learning;
computing an empirical distribution for each of the pre-determined number of candidates; and
determining the first set of labels based on the computed empirical distributions.
8 . A system having at least one processor, storage, and a communication platform connected to a network for multi-label prediction, the system comprising:
a multi-label learning engine implemented on the at least one processor and configured to generate a label space;
a first feature extractor implemented on the at least one processor and configured to generate a first feature vector from a data point received from a user;
a projecting unit implemented on the at least one processor and configured to project the first feature vector to the label space;
a predicting unit implemented on the at least one processor and configured to determine a first set of labels associated with the first feature vector from the label space;
a label generator implemented on the at least one processor and configured to convert the first set of labels to a second set of labels; and
a presenting unit implemented on the at least one processor and configured to provide the second set of labels to the user.
9 . The system of claim 8 , wherein the multi-label learning engine implemented on the at least one processor further comprises:
a data sampler configured to obtain a plurality of data samples from at least a knowledge base;
a second feature extractor configured to generate a plurality of second feature vectors respectively associated with the plurality of data samples;
a label extractor configured to extract one or more second labels associated with the plurality of second feature vectors;
a label space generator configured to generate a first label matrix based on the plurality of second feature vectors and the one or more second labels;
a dimension reducer configured to transform the first label matrix to a second label matrix;
a learning unit configured to train one or more parameters associated with the second label matrix, and generate the label space based on the second label matrix and the trained one or more parameters.
10 . The system of claim 9 , wherein each element of the first label matrix indicates a relation as to whether one of the plurality of second vectors is annotated by one of the one or more second labels.
11 . The system of claim 9 , wherein the dimension reducer is further configured to:
perform dimensionality reduction on the first label matrix based on random rejection, wherein a first dimension of the first label matrix representing a number of labels is reduced to a pre-determined value in the second label matrix.
12 . The system of claim 9 , wherein the one or more parameters associated with the second label matrix is trained by a least square regression model.
13 . The system of claim 9 , wherein the first feature vector is projected to the label space using the one or more parameters associated with the second label matrix.
14 . The system of claim 8 , wherein the predicting unit is further configured to:
select a pre-determined number of candidates from the label space using k-nearest neighbor learning;
compute an empirical distribution for each of the pre-determined number of candidates; and
determine the first set of labels based on the computed empirical distributions.
15 . A non-transitory machine-readable medium having information recorded thereon for multi-label prediction, wherein the information, when read by the machine, causes the machine to perform the following:
generating a label space;
receiving a data point from a user;
generating a first feature vector from the data point;
projecting the first feature vector to the label space;
determining a first set of labels associated with the first feature vector from the label space;
converting the first set of labels to a second set of labels; and
providing the second set of labels to the user.
16 . The medium of claim 15 , wherein the information, when read by the machine, causes the machine to further perform the following:
obtaining a plurality of data samples from at least a knowledge base;
generating a plurality of second feature vectors respectively associated with the plurality of data samples;
extracting one or more second labels associated with the plurality of second feature vectors;
generating a first label matrix based on the plurality of second feature vectors and the one or more second labels;
transforming the first label matrix to a second label matrix;
training one or more parameters associated with the second label matrix; and
generating the label space based on the second label matrix and the trained one or more parameters.
17 . The medium of claim 16 , wherein each element of the first label matrix indicates a relation as to whether one of the plurality of second vectors is annotated by one of the one or more second labels.
18 . The medium of claim 16 , wherein the information, when read by the machine, causes the machine to further perform the following:
performing dimensionality reduction on the first label matrix based on random rejection, wherein a first dimension of the first label matrix representing a number of labels is reduced to a pre-determined value in the second label matrix.
19 . The medium of claim 16 , wherein the one or more parameters associated with the second label matrix is trained by a least square regression model.
20 . The medium of claim 15 , wherein the information, when read by the machine, causes the machine to further perform the following:
selecting a pre-determined number of candidates from the label space using k-nearest neighbor learning;
computing an empirical distribution for each of the pre-determined number of candidates; and
determining the first set of labels based on the computed empirical distributions.