[SPARK-30109][ML] PCA use BLAS.gemv for sparse vectors

### What changes were proposed in this pull request?
When PCA was first impled in [SPARK-5521](https://issues.apache.org/jira/browse/SPARK-5521), at that time Matrix.multiply(BLAS.gemv internally) did not support sparse vector. So worked around it by applying a sparse matrix multiplication.

Since [SPARK-7681](https://issues.apache.org/jira/browse/SPARK-7681), BLAS.gemv supported sparse vector. So we can directly use Matrix.multiply now.

### Why are the changes needed?
for simplity

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
existing testsuites

Closes apache#26745 from zhengruifeng/pca_mul.

Authored-by: zhengruifeng <[email protected]>
Signed-off-by: zhengruifeng <[email protected]>

Author: zhengruifeng

mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala

@@ -147,22 +147,8 @@ class PCAModel private[ml] (
   override def transform(dataset: Dataset[_]): DataFrame = {
     transformSchema(dataset.schema, logging = true)
 
-    val func = { vector: Vector =>
-      vector match {
-        case dv: DenseVector =>
-          pc.transpose.multiply(dv)
-        case SparseVector(size, indices, values) =>
-          /* SparseVector -> single row SparseMatrix */
-          val sm = Matrices.sparse(size, 1, Array(0, indices.length), indices, values).transpose
-          val projection = sm.multiply(pc)
-          Vectors.dense(projection.values)
-        case _ =>
-          throw new IllegalArgumentException("Unsupported vector format. Expected " +
-            s"SparseVector or DenseVector. Instead got: ${vector.getClass}")
-      }
-    }
-
-    val transformer = udf(func)
+    val transposed = pc.transpose
+    val transformer = udf { vector: Vector => transposed.multiply(vector) }
     dataset.withColumn($(outputCol), transformer(col($(inputCol))))
   }
 

mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala

@@ -111,18 +111,7 @@ class PCAModel private[spark] (
    */
   @Since("1.4.0")
   override def transform(vector: Vector): Vector = {
-    vector match {
-      case dv: DenseVector =>
-        pc.transpose.multiply(dv)
-      case SparseVector(size, indices, values) =>
-        /* SparseVector -> single row SparseMatrix */
-        val sm = Matrices.sparse(size, 1, Array(0, indices.length), indices, values).transpose
-        val projection = sm.multiply(pc)
-        Vectors.dense(projection.values)
-      case _ =>
-        throw new IllegalArgumentException("Unsupported vector format. Expected " +
-          s"SparseVector or DenseVector. Instead got: ${vector.getClass}")
-    }
+    pc.transpose.multiply(vector)
   }
 }